Posts in category: Amazon AWS

This is a guest post co-authored by Shravan Kumar and Avirat S from Gramener.

Gramener, a Straive company, contributes to sustainable development by focusing on agriculture, forestry, water management, and renewable energy. By providing authorities with the tools and insights they need to make informed decisions about environmental and social impact, Gramener is playing a vital role in building a more sustainable future.

Urban heat islands (UHIs) are areas within cities that experience significantly higher temperatures than their surrounding rural areas. UHIs are a growing concern because they can lead to various environmental and health issues. To address this challenge, Gramener has developed a solution that uses spatial data and advanced modeling techniques to understand and mitigate the following UHI effects:

• Temperature discrepancy – UHIs can cause urban areas to be hotter than their surrounding rural regions.
• Health impact – Higher temperatures in UHIs contribute to a 10-20% increase in heat-related illnesses and fatalities.
• Energy consumption – UHIs amplify air conditioning demands, resulting in an up to 20% surge in energy consumption.
• Air quality – UHIs worsen air quality, leading to elevated levels of smog and particulate matter, which can increase respiratory problems.
• Economic impact – UHIs can result in billions of dollars in additional energy costs, infrastructure damage, and healthcare expenditures.

Gramener’s GeoBox solution empowers users to effortlessly tap into and analyze public geospatial data through its powerful API, enabling seamless integration into existing workflows. This streamlines exploration and saves valuable time and resources, allowing communities to quickly identify UHI hotspots. GeoBox then transforms raw data into actionable insights presented in user-friendly formats like raster, GeoJSON, and Excel, ensuring clear understanding and immediate implementation of UHI mitigation strategies. This empowers communities to make informed decisions and implement sustainable urban development initiatives, ultimately supporting citizens through improved air quality, reduced energy consumption, and a cooler, healthier environment.

This post demonstrates how Gramener’s GeoBox solution uses Amazon SageMaker geospatial capabilities to perform earth observation analysis and unlock UHI insights from satellite imagery. SageMaker geospatial capabilities make it straightforward for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. SageMaker geospatial capabilities allow you to efficiently transform and enrich large-scale geospatial datasets, and accelerate product development and time to insight with pre-trained ML models.

Solution overview

Geobox aims to analyze and predict the UHI effect by harnessing spatial characteristics. It helps in understanding how proposed infrastructure and land use changes can impact UHI patterns and identifies the key factors influencing UHI. This analytical model provides accurate estimates of land surface temperature (LST) at a granular level, allowing Gramener to quantify changes in the UHI effect based on parameters (names of indexes and data used).

Geobox enables city departments to do the following:

• Improved climate adaptation planning – Informed decisions reduce the impact of extreme heat events.
• Support for green space expansion – More green spaces enhance air quality and quality of life.
• Enhanced interdepartmental collaboration – Coordinated efforts improve public safety.
• Strategic emergency preparedness – Targeted planning reduces the potential for emergencies.
• Health services collaboration – Cooperation leads to more effective health interventions.

Solution workflow

In this section, we discuss how the different components work together, from data acquisition to spatial modeling and forecasting, serving as the core of the UHI solution. The solution follows a structured workflow, with a primary focus on addressing UHIs in a city of Canada.

Phase 1: Data pipeline

The Landsat 8 satellite captures detailed imagery of the area of interest every 15 days at 11:30 AM, providing a comprehensive view of the city’s landscape and environment. A grid system is established with a 48-meter grid size using Mapbox’s Supermercado Python library at zoom level 19, enabling precise spatial analysis.

Phase 2: Exploratory analysis

Integrating infrastructure and population data layers, Geobox empowers users to visualize the city’s variable distribution and derive urban morphological insights, enabling a comprehensive analysis of the city’s structure and development.

Also, Landsat imagery from phase 1 is used to derive insights like the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI), with data meticulously scaled to the 48-meter grid for consistency and accuracy.

The following variables are used:

• Land surface temperature
• Building site coverage
• NDVI
• Building block coverage
• NDBI
• Building area
• Albedo
• Building count
• Modified Normalized Difference Water Index (MNDWI)
• Building height
• Number of floors and floor area
• Floor area ratio

Phase 3: Analytics model

This phase comprises three modules, employing ML models on data to gain insights into LST and its relationship with other influential factors:

• Module 1: Zonal statistics and aggregation – Zonal statistics play a vital role in computing statistics using values from the value raster. It involves extracting statistical data for each zone based on the zone raster. Aggregation is performed at a 100-meter resolution, allowing for a comprehensive analysis of the data.
• Module 2: Spatial modeling – Gramener evaluated three regression models (linear, spatial, and spatial fixed effects) to unravel the correlation between Land Surface Temperature (LST) and other variables. Among these models, the spatial fixed effect model yielded the highest mean R-squared value, particularly for the timeframe spanning 2014 to 2020.
• Module 3: Variables forecasting – To forecast variables in the short term, Gramener employed exponential smoothing techniques. These forecasts aided in understanding future LST values and their trends. Additionally, they delved into long-term scale analysis by using Representative Concentration Pathway (RCP8.5) data to predict LST values over extended periods.

Data acquisition and preprocessing

To implement the modules, Gramener used the SageMaker geospatial notebook within Amazon SageMaker Studio. The geospatial notebook kernel is pre-installed with commonly used geospatial libraries, enabling direct visualization and processing of geospatial data within the Python notebook environment.

Gramener employed various datasets to predict LST trends, including building assessment and temperature data, as well as satellite imagery. The key to the UHI solution was using data from the Landsat 8 satellite. This Earth-imaging satellite, a joint venture of USGS and NASA, served as a fundamental component in the project.

With the SearchRasterDataCollection API, SageMaker provides a purpose-built functionality to facilitate the retrieval of satellite imagery. Gramener used this API to retrieve Landsat 8 satellite data for the UHI solution.

The SearchRasterDataCollection API uses the following input parameters:

• Arn – The Amazon Resource Name (ARN) of the raster data collection used in the query
• AreaOfInterest – A GeoJSON polygon representing the area of interest
• TimeRangeFilter – The time range of interest, denoted as {StartTime: <string>, EndTime: <string>}
• PropertyFilters – Supplementary property filters, such as specifications for maximum acceptable cloud cover, can also be incorporated

The following example demonstrates how Landsat 8 data can be queried via the API:

search_params = {
    "Arn": "arn:aws:sagemaker-geospatial:us-west-2:378778860802:raster-data-collection/public/gmqa64dcu2g9ayx1", # NASA/USGS Landsat
    "RasterDataCollectionQuery": {
        "AreaOfInterest": {
            "AreaOfInterestGeometry": {
                "PolygonGeometry": {
                    "Coordinates": coordinates
                }
            }
        },
        "TimeRangeFilter": {
            "StartTime": "2014-01-01T00:00:00Z",
            "EndTime": "2020-12-31T23:59:59Z",
        },
        "PropertyFilters": {
            "Properties": [{"Property": {"EoCloudCover": {"LowerBound": 0, "UpperBound": 20.0}}}],
            "LogicalOperator": "AND",
        }
    },
}

response = geospatial_client.search_raster_data_collection(**search_params)

To process large-scale satellite data, Gramener used Amazon SageMaker Processing with the geospatial container. SageMaker Processing enables the flexible scaling of compute clusters to accommodate tasks of varying sizes, from processing a single city block to managing planetary-scale workloads. Traditionally, manually creating and managing a compute cluster for such tasks was both costly and time-consuming, particularly due to the complexities involved in standardizing an environment suitable for geospatial data handling.

Now, with the specialized geospatial container in SageMaker, managing and running clusters for geospatial processing has become more straightforward. This process requires minimal coding effort: you simply define the workload, specify the location of the geospatial data in Amazon Simple Storage Service (Amazon S3), and select the appropriate geospatial container. SageMaker Processing then automatically provisions the necessary cluster resources, facilitating the efficient run of geospatial tasks on scales that range from city level to continent level.

SageMaker fully manages the underlying infrastructure required for the processing job. It allocates cluster resources for the duration of the job and removes them upon job completion. Finally, the results of the processing job are saved in the designated S3 bucket.

A SageMaker Processing job using the geospatial image can be configured as follows from within the geospatial notebook:

from sagemaker import get_execution_role
from sagemaker.sklearn.processing import ScriptProcessor
from sagemaker.processing import ProcessingInput, ProcessingOutput

execution_role_arn = get_execution_role()

geospatial_image_uri = '081189585635.dkr.ecr.us-west-2.amazonaws.com/sagemaker-geospatial-v1-0:latest'
processor = ScriptProcessor(
    command=['python3'],
    image_uri=geospatial_image_uri,
    role=execution_role_arn,
    instance_count=20,
    instance_type='ml.m5.xlarge',
    base_job_name='geospatial-processing-spectral-indices'
)

The instance_count parameter defines how many instances the processing job should use, and the instance_type defines what type of instance should be used.

The following example shows how a Python script is run on the processing job cluster. When the run command is invoked, the cluster starts up and automatically provisions the necessary cluster resources:

processor.run(
    code='calculate_variables.py',
    inputs=[
        ProcessingInput(
            source=s3_manifest_url,
            destination='/opt/ml/processing/input_data/',
            s3_data_type="ManifestFile",
            s3_data_distribution_type="ShardedByS3Key"
        ),
    ],
    outputs=[
        ProcessingOutput(
            source='/opt/ml/processing/output_data/',
            destination=s3_output_prefix_url
        )
    ]
)

Spatial modeling and LST predictions

In the processing job, a range of variables, including top-of-atmosphere spectral radiance, brightness temperature, and reflectance from Landsat 8, are computed. Additionally, morphological variables such as floor area ratio (FAR), building site coverage, building block coverage, and Shannon’s Entropy Value are calculated.

The following code demonstrates how this band arithmetic can be performed:

def calculate_ndvi(nir08, red): 
    return (nir08 - red) / (nir08 + red) 
 
def calculate_ndbi(swir16, nir08): 
    return (swir16 - nir08) / (swir16 + nir08) 
 
def calculate_st(bt): 
    return ((bt * 0.00341802) + 149.0) - 273 
 
def indices_calc(data): 
    with concurrent.futures.ThreadPoolExecutor() as executor: 
        ndvi_future = executor.submit(calculate_ndvi, data.sel(band="SR_B5"), data.sel(band="SR_B4")) 
        ndbi_future = executor.submit(calculate_ndbi, data.sel(band="SR_B6"), data.sel(band="SR_B5")) 
        st_future = executor.submit(calculate_st, data.sel(band="ST_B10")) 
 
        ndvi = ndvi_future.result() 
        ndbi = ndbi_future.result() 
        st = st_future.result() 
 
    ndvi.attrs = data.attrs 
    ndbi.attrs = data.attrs 
    st.attrs = data.attrs 
 
    return ndvi, ndbi, st 

After the variables have been calculated, zonal statistics are performed to aggregate data by grid. This involves calculating statistics based on the values of interest within each zone. For these computations a grid size of approximately 100 meters has been used.

def process_iteration(st, ndvi, ndmi, date, city_name): 
    datacube['st'] = (st.dims, st.values) 
    datacube['ndvi'] = (ndvi.dims, ndvi.values) 
    datacube['ndmi'] = (ndmi.dims, ndmi.values) 
    df = datacube.groupby("id").mean().to_dataframe().reset_index() 
    merged_grid = hexgrid_utm.join(df, on='id', how='left', lsuffix='_')[['id', 'hex_id', 'geometry', 'st', 'ndvi', 'ndmi']] 
    merged_grid.to_file(f"{DATA}/{city_name}/{city_name}_outputs_{date}.geojson", driver='GeoJSON') 
    print("Working on:", date) 
 
def iterative_op(city_json, st, ndvi, ndmi, city_name): 
    with concurrent.futures.ThreadPoolExecutor() as executor: 
        futures = [ 
            executor.submit(process_iteration, st[i], ndvi[i], ndmi[i], date, city_name) 
            for i, _ in enumerate(city_json.time) 
            for date in city_json.date 
        ] 
        for future in concurrent.futures.as_completed(futures): 
            future.result() 
 
    print('Process completed') 

After aggregating the data, spatial modeling is performed. Gramener used spatial regression methods, such as linear regression and spatial fixed effects, to account for spatial dependence in the observations. This approach facilitates modeling the relationship between variables and LST at a micro level.

The following code illustrates how such spatial modeling can be run:

features = [ 
    'ndvi', 
    'ndbi', 
    'st', 
    'build_count', 
    'bbc' 
] 
 
def compute_spatial_weights(df, k=8): 
    knn = KNN.from_dataframe(df, k=k) 
    return df[features].apply(lambda y: weights.spatial_lag.lag_spatial(knn, y)).rename(columns=lambda c: 'w_' + c) 
 
def ordinary_least_squares(df_year, spatial=False): 
    formula = f"lst ~ {' + '.join(features)}"  
    if spatial: 
        df_year = df_year.join(compute_spatial_weights(df_year)) 
        formula += f" + {' + '.join(['w_' + f for f in features])}"  
     
    return smf.ols(formula, data=df_year).fit() 
 
def process(df, year): 
    df_year = pd.merge(df[df['year'] == year].fillna(0), grids[['idx', 'name']], on='idx') 
    ols_model = ordinary_least_squares(df_year) 
    ols_spatial_model = ordinary_least_squares(df_year, spatial=True) 
    ols_spatial_fe_model = ordinary_least_squares(df_year, spatial=True) 
     
    return { 
        'year': year, 
        'ols_model': ols_model, 
        'ols_spatial_model': ols_spatial_model, 
        'ols_spatial_fe_model': ols_spatial_fe_model, 
        'ols_r2': [ols_model.rsquared, ols_spatial_model.rsquared, ols_spatial_fe_model.rsquared] 
    } 

Gramener used exponential smoothing to predict the LST values. Exponential smoothing is an effective method for time series forecasting that applies weighted averages to past data, with the weights decreasing exponentially over time. This method is particularly effective in smoothing out data to identify trends and patterns. By using exponential smoothing, it becomes possible to visualize and predict LST trends with greater precision, allowing for more accurate predictions of future values based on historical patterns.

To visualize the predictions, Gramener used the SageMaker geospatial notebook with open-source geospatial libraries to overlay model predictions on a base map and provides layered visualize geospatial datasets directly within the notebook.

Conclusion

This post demonstrated how Gramener is empowering clients to make data-driven decisions for sustainable urban environments. With SageMaker, Gramener achieved substantial time savings in UHI analysis, reducing processing time from weeks to hours. This rapid insight generation allows Gramener’s clients to pinpoint areas requiring UHI mitigation strategies, proactively plan urban development and infrastructure projects to minimize UHI, and gain a holistic understanding of environmental factors for comprehensive risk assessment.

Discover the potential of integrating Earth observation data in your sustainability projects with SageMaker. For more information, refer to Get started with Amazon SageMaker geospatial capabilities.

About the Authors

Abhishek Mittal is a Solutions Architect for the worldwide public sector team with Amazon Web Services (AWS), where he primarily works with ISV partners across industries providing them with architectural guidance for building scalable architecture and implementing strategies to drive adoption of AWS services. He is passionate about modernizing traditional platforms and security in the cloud. Outside work, he is a travel enthusiast.

Janosch Woschitz is a Senior Solutions Architect at AWS, specializing in AI/ML. With over 15 years of experience, he supports customers globally in leveraging AI and ML for innovative solutions and building ML platforms on AWS. His expertise spans machine learning, data engineering, and scalable distributed systems, augmented by a strong background in software engineering and industry expertise in domains such as autonomous driving.

Shravan Kumar is a Senior Director of Client success at Gramener, with decade of experience in Business Analytics, Data Evangelism & forging deep Client Relations. He holds a solid foundation in Client Management, Account Management within the realm of data analytics, AI & ML.

Avirat S is a geospatial data scientist at Gramener, leveraging AI/ML to unlock insights from geographic data. His expertise lies in disaster management, agriculture, and urban planning, where his analysis informs decision-making processes.

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With a multitude of articles, videos, audio recordings, and other media created daily across news media companies, readers of all types—individual consumers, corporate subscribers, and more—often find it difficult to find news content that is most relevant to them. Delivering personalized news and experiences to readers can help solve this problem, and create more engaging experiences. However, delivering truly personalized recommendations presents several key challenges:

• Capturing diverse user interests – News can span many topics and even within specific topics, readers can have varied interests.
• Addressing limited reader history – Many news readers have sparse activity histories. Recommenders must quickly learn preferences from limited data to provide value.
• Timeliness and trending – Daily news cycles mean recommendations must balance personalized content with the discovery of new, popular stories.
• Changing interests – Readers’ interests can evolve over time. Systems have to detect shifts and adapt recommendations accordingly.
• Explainability – Providing transparency into why certain stories are recommended builds user trust. The ideal news recommendation system understands the individual and responds to the broader news climate and audience. Tackling these challenges is key to effectively connecting readers with content they find informative and engaging.

In this post, we describe how Amazon Personalize can power a scalable news recommender application. This solution was implemented at a Fortune 500 media customer in H1 2023 and can be reused for other customers interested in building news recommenders.

Solution overview

Amazon Personalize is a great fit to power a news recommendation engine because of its ability to provide real-time and batch personalized recommendations at scale. Amazon Personalize offers a variety of recommendation recipes (algorithms), such as the User Personalization and Trending Now recipes, which are particularly suitable for training news recommender models. The User Personalization recipe analyzes each user’s preferences based on their engagement with content over time. This results in customized news feeds that surface the topics and sources most relevant to an individual user. The Trending Now recipe complements this by detecting rising trends and popular news stories in real time across all users. Combining recommendations from both recipes allows the recommendation engine to balance personalization with the discovery of timely, high-interest stories.

The following diagram illustrates the architecture of a news recommender application powered by Amazon Personalize and supporting AWS services.

This solution has the following limitations:

• Providing personalized recommendations for just-published articles (articles published a few minutes ago) can be challenging. We describe how to mitigate this limitation later in this post.
• Amazon Personalize has a fixed number of interactions and items dataset features that can be used to train a model.
• At the time of writing, Amazon Personalize doesn’t provide recommendation explanations at the user level.

Let’s walk through each of the main components of the solution.

Prerequisites

To implement this solution, you need the following:

• Historical and real-time user click data for the interactions dataset
• Historical and real-time news article metadata for the items dataset

Ingest and prepare the data

To train a model in Amazon Personalize, you need to provide training data. In this solution, you use two types of Amazon Personalize training datasets: the interactions dataset and items dataset. The interactions dataset contains data on user-item-timestamp interactions, and the items dataset contains features on the recommended articles.

You can take two different approaches to ingest training data:

• Batch ingestion – You can use AWS Glue to transform and ingest interactions and items data residing in an Amazon Simple Storage Service (Amazon S3) bucket into Amazon Personalize datasets. AWS Glue performs extract, transform, and load (ETL) operations to align the data with the Amazon Personalize datasets schema. When the ETL process is complete, the output file is placed back into Amazon S3, ready for ingestion into Amazon Personalize via a dataset import job.
• Real-time ingestion – You can use Amazon Kinesis Data Streams and AWS Lambda to ingest real-time data incrementally. A Lambda function performs the same data transformation operations as the batch ingestion job at the individual record level, and ingests the data into Amazon Personalize using the PutEvents and PutItems APIs.

In this solution, you can also ingest certain items and interactions data attributes into Amazon DynamoDB. You can use these attributes during real-time inference to filter recommendations by business rules. For example, article metadata may contain company and industry names in the article. To proactively recommend articles on companies or industries that users are reading about, you can record how frequently readers are engaging with articles about specific companies and industries, and use this data with Amazon Personalize filters to further tailor the recommended content. We discuss more about how to use items and interactions data attributes in DynamoDB later in this post.

The following diagram illustrates the data ingestion architecture.

Train the model

The bulk of the model training effort should focus on the User Personalization model, because it can use all three Amazon Personalize datasets (whereas the Trending Now model only uses the interactions dataset). We recommend running experiments that systematically vary different aspects of the training process. For the customer that implemented this solution, the team ran over 30 experiments. This included modifying the interactions and items dataset features, adjusting the length of interactions history provided to the model, tuning Amazon Personalize hyperparameters, and evaluating whether an explicit user’s dataset improved offline performance (relative to the increase in training time).

Each model variation was evaluated based on metrics reported by Amazon Personalize on the training data, as well as custom offline metrics on a holdout test dataset. Standard metrics to consider include mean average precision (MAP) @ K (where K is the number of recommendations presented to a reader), normalized discounted cumulative gain, mean reciprocal rank, and coverage. For more information about these metrics, see Evaluating a solution version with metrics. We recommend prioritizing MAP @ K out of these metrics, which captures the average number of articles a reader clicked on out of the top K articles recommended to them, because the MAP metric is a good proxy for (real) article clickthrough rates. K should be selected based on the number of articles a reader can view on a desktop or mobile webpage without having to scroll, allowing you to evaluate recommendation effectiveness with minimal reader effort. Implementing custom metrics, such as recommendation uniqueness (which describes how unique the recommendation output was across the pool of candidate users), can also provide insight into recommendation effectiveness.

With Amazon Personalize, the experimental process allows you to determine the optimal set of dataset features for both the User Personalization and Trending Now models. The Trending Now model exists within the same Amazon Personalize dataset group as the User Personalization model, so it uses the same set of interactions dataset features.

Generate real-time recommendations

When a reader visits a news company’s webpage, an API call will be made to the news recommender via Amazon API Gateway. This triggers a Lambda function that calls the Amazon Personalize models’ endpoints to get recommendations in real time. During inference, you can use filters to filter the initial recommendation output based on article or reader interaction attributes. For example, if “News Topic” (such as sports, lifestyle, or politics) is an article attribute, you can restrict recommendations to specific news topics if that is a product requirement. Similarly, you can use filters on reader interaction events, such as excluding articles a reader has already read.

One key challenge with real-time recommendations is effectively including just-published articles (also called cold items) into the recommendation output. Just-published articles don’t have any historical interaction data that recommenders normally rely on, and recommendation systems need sufficient processing time to assess how relevant just-published articles are to a specific user (even if only using user-item relationship signals).

Amazon Personalize can natively auto detect and recommend new articles ingested into the items dataset every 2 hours. However, because this use case is focused on news recommendations, you need a way to recommend new articles as soon as they’re published and ready for reader consumption.

One way to solve this problem is by designing a mechanism to randomly insert just-published articles into the final recommendation output for each reader. You can add a feature to control what percent of articles in the final recommendation set were just-published articles, and similar to the original recommendation output from Amazon Personalize, you can filter just-published articles by article attributes (such as “News Topic”) if it is a product requirement. You can track interactions on just-published articles in DynamoDB as they start trickling in to the system, and prioritize the most popular just-published articles during recommendation postprocessing, until the just-published articles are detected and processed by the Amazon Personalize models.

After you have your final set of recommended articles, this output is submitted to another postprocessing Lambda function that checks the output to see if it aligns with pre-specified business rules. These can include checking whether recommended articles meet webpage layout specifications, if recommendations are served in a web browser frontend, for example. If needed, articles can be reranked to ensure business rules are met. We recommend reranking by implementing a function that allows higher-ranking articles to only fall down in ranking one place at a time until all business rules are met, providing minimal relevancy loss for readers. The final list of postprocessed articles is returned to the web service that initiated the request for recommendations.

The following diagram illustrates the architecture for this step in the solution.

Generate batch recommendations

Personalized news dashboards (through real-time recommendations) require a reader to actively search for news, but in our busy lives today, sometimes it’s just easier to have your top news sent to you. To deliver personalized news articles as an email digest, you can use an AWS Step Functions workflow to generate batch recommendations. The batch recommendation workflow gathers and postprocesses recommendations from our User Personalization model or Trending Now model endpoints, giving flexibility to select what combination of personalized and trending articles teams want to push to their readers. Developers also have the option of using the Amazon Personalize batch inference feature; however, at the time of writing, creating an Amazon Personalize batch inference job doesn’t support including items ingested after an Amazon Personalize custom model has been trained, and it doesn’t support the Trending Now recipe.

During a batch inference Step Functions workflow, the list of readers is divided into batches, processed in parallel, and submitted to a postprocessing and validation layer before being sent to the email generation service. The following diagram illustrates this workflow.

Scale the recommender system

To effectively scale, you also need the news recommender to accommodate a growing number of users and increased traffic without creating any degradation in reader experience. Amazon Personalize model endpoints natively auto scale to meet increased traffic. Engineers only need to set and monitor a minimum provisioned transactions per second (TPS) variable for each Amazon Personalize endpoint.

Beyond Amazon Personalize, the news recommender application presented here is built using serverless AWS services, allowing engineering teams to focus on delivering the best reader experience without worrying about infrastructure maintenance.

Conclusion

In this attention economy, it has become increasingly important to deliver relevant and timely content for consumers. In this post, we discussed how you can use Amazon Personalize to build a scalable news recommender, and the strategies organizations can implement to address the unique challenges of delivering news recommendations.

To learn more about Amazon Personalize and how it can help your organization build recommendation systems, check out the Amazon Personalize Developer Guide.

Happy building!

About the Authors

Bala Krishnamoorthy is a Senior Data Scientist at AWS Professional Services, where he helps customers build and deploy AI-powered solutions to solve their business challenges. He has worked with customers across diverse sectors, including media & entertainment, financial services, healthcare, and technology. In his free time, he enjoys spending time with family/friends, staying active, trying new restaurants, travel, and kickstarting his day with a steaming hot cup of coffee.

Rishi Jala is a NoSQL Data Architect with AWS Professional Services. He focuses on architecting and building highly scalable applications using NoSQL databases such as Amazon DynamoDB. Passionate about solving customer problems, he delivers tailored solutions to drive success in the digital landscape.

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This is a guest post co-written with Tamir Rubinsky and Aviad Aranias from Nielsen Sports.

Nielsen Sports shapes the world’s media and content as a global leader in audience insights, data, and analytics. Through our understanding of people and their behaviors across all channels and platforms, we empower our clients with independent and actionable intelligence so they can connect and engage with their audiences—now and into the future.

At Nielsen Sports, our mission is to provide our customers—brands and rights holders—with the ability to measure the return on investment (ROI) and effectiveness of a sport sponsorship advertising campaign across all channels, including TV, online, social media, and even newspapers, and to provide accurate targeting at local, national, and international levels.

In this post, we describe how Nielsen Sports modernized a system running thousands of different machine learning (ML) models in production by using Amazon SageMaker multi-model endpoints (MMEs) and reduced operational and financial cost by 75%.

Challenges with channel video segmentation

Our technology is based on artificial intelligence (AI) and specifically computer vision (CV), which allows us to track brand exposure and identify its location accurately. For example, we identify if the brand is on a banner or a shirt. In addition, we identify the location of the brand on the item, such as the top corner of a sign or the sleeve. The following figure shows an example of our tagging system.

To understand our scaling and cost challenges, let’s look at some representative numbers. Every month, we identify over 120 million brand impressions across different channels, and the system must support the identification of over 100,000 brands and variations of different brands. We have built one of the largest databases of brand impressions in the world with over 6 billion data points.

Our media evaluation process includes several steps, as illustrated in the following figure:

1. First, we record thousands of channels around the world using an international recording system.
2. We stream the content in combination with the broadcast schedule (Electronic Programming Guide) to the next stage, which is segmentation and separation between the game broadcasts themselves and other content or advertisements.
3. We perform media monitoring, where we add additional metadata to each segment, such as league scores, relevant teams, and players.
4. We perform an exposure analysis of the brands’ visibility and then combine the audience information to calculate the valuation of the campaign.
5. The information is delivered to the customer by a dashboard or analyst reports. The analyst is given direct access to the raw data or through our data warehouse.

Because we operate at a scale of over a thousand channels and tens of thousands of hours of video a year, we must have a scalable automation system for the analysis process. Our solution automatically segments the broadcast and knows how to isolate the relevant video clips from the rest of the content.

We do this using dedicated algorithms and models developed by us for analyzing the specific characteristics of the channels.

In total, we are running thousands of different models in production to support this mission, which is costly, incurs operational overhead, and is error-prone and slow. It took months to get models with new model architecture to production.

This is where we wanted to innovate and rearchitect our system.

Cost-effective scaling for CV models using SageMaker MMEs

Our legacy video segmentation system was difficult to test, change, and maintain. Some of the challenges include working with an old ML framework, inter-dependencies between components, and a hard-to-optimize workflow. This is because we were based on RabbitMQ for the pipeline, which was a stateful solution. To debug one component, such as feature extraction, we had to test all of the pipeline.

The following diagram illustrates the previous architecture.

As part of our analysis, we identified performance bottlenecks such as running a single model on a machine, which showed a low GPU utilization of 30–40%. We also discovered inefficient pipeline runs and scheduling algorithms for the models.

Therefore, we decided to build a new multi-tenant architecture based on SageMaker, which would implement performance optimization improvements, support dynamic batch sizes, and run multiple models simultaneously.

Each run of the workflow targets a group of videos. Each video is between 30–90 minutes long, and each group has more than five models to run.

Let’s examine an example: a video can be 60 minutes long, consisting of 3,600 images, and each image needs to inferred by three different ML models during the first stage. With SageMaker MMEs, we can run batches of 12 images in parallel, and the full batch completes in less than 2 seconds. In a regular day, we have more than 20 groups of videos, and on a packed weekend day, we can have more than 100 groups of videos.

The following diagram shows our new, simplified architecture using a SageMaker MME.

Results

With the new architecture, we achieved many of our desired outcomes and some unseen advantages over the old architecture:

• Better runtime – By increasing batch sizes (12 videos in parallel) and running multiple models concurrently (five models in parallel), we have decreased our overall pipeline runtime by 33%, from 1 hour to 40 minutes.
• Improved infrastructure – With SageMaker, we upgraded our existing infrastructure, and we are now using newer AWS instances with newer GPUs such as g5.xlarge. One of the biggest benefits from the change is the immediate performance improvement from using TorchScript and CUDA optimizations.
• Optimized infrastructure usage – By having a single endpoint that can host multiple models, we can reduce both the number of endpoints and the number of machines we need to maintain, and also increase the utilization of a single machine and its GPU. For a specific task with five videos, we now use only five machines of g5 instances, which gives us 75% cost benefit from the previous solution. For a typical workload during the day, we use a single endpoint with a single machine of g5.xlarge with a GPU utilization of more than 80%. For comparison, the previous solution had less than 40% utilization.
• Increased agility and productivity – Using SageMaker allowed us to spend less time migrating models and more time improving our core algorithms and models. This has increased productivity for our engineering and data science teams. We can now research and deploy a new ML model in under 7 days, instead of over 1 month previously. This is a 75% improvement in velocity and planning.
• Better quality and confidence – With SageMaker A/B testing capabilities, we can deploy our models in a gradual way and be able to safely roll back. The faster lifecycle to production also increased our ML models’ accuracy and results.

The following figure shows our GPU utilization with the previous architecture (30–40% GPU utilization).

The following figure shows our GPU utilization with the new simplified architecture (90% GPU utilization).

Conclusion

In this post, we shared how Nielsen Sports modernized a system running thousands of different models in production by using SageMaker MMEs and reduced their operational and financial cost by 75%.

For further reading, refer to the following:

• Model hosting patterns in Amazon SageMaker, Part 1: Common design patterns for building ML applications on Amazon SageMaker
• Model hosting patterns in Amazon SageMaker, Part 3: Run and optimize multi-model inference with Amazon SageMaker multi-model endpoints
• Load Testing SageMaker Multi-Model Endpoints

About the Authors

Eitan Sela is a Generative AI and Machine Learning Specialist Solutions Architect with Amazon Web Services. He works with AWS customers to provide guidance and technical assistance, helping them build and operate Generative AI and Machine Learning solutions on AWS. In his spare time, Eitan enjoys jogging and reading the latest machine learning articles.

Gal Goldman is a Senior Software Engineer and an Enterprise Senior Solution Architect in AWS with a passion for cutting-edge solutions. He specializes in and has developed many distributed Machine Learning services and solutions. Gal also focuses on helping AWS customers accelerate and overcome their engineering and Generative AI challenges.

Tal Panchek is a Senior Business Development Manager for Artificial Intelligence and Machine Learning with Amazon Web Services. As a BD Specialist, he is responsible for growing adoption, utilization, and revenue for AWS services. He gathers customer and industry needs and partner with AWS product teams to innovate, develop, and deliver AWS solutions.

Tamir Rubinsky leads Global R&D Engineering at Nielsen Sports, bringing vast experience in building innovative products and managing high-performing teams. His work transformed sports sponsorship media evaluation through innovative, AI-powered solutions.

Aviad Aranias is a MLOps Team Leader and Nielsen Sports Analysis Architect who specializes in crafting complex pipelines for analyzing sports event videos across numerous channels. He excels in building and deploying deep learning models to handle large-scale data efficiently. In his spare time, he enjoys baking delicious Neapolitan pizzas.

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Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. SageMaker Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity.

Amazon SageMaker Canvas is a powerful no-code ML tool designed for business and data teams to generate accurate predictions without writing code or having extensive ML experience. With its intuitive visual interface, SageMaker Canvas simplifies the process of loading, cleansing, and transforming datasets, and building ML models, making it accessible to a broader audience.

However, as your ML needs evolve, or if you require more advanced customization and control, you may want to transition from a no-code environment to a code-first approach. This is where the seamless integration between SageMaker Canvas and SageMaker Studio comes into play.

In this post, we present a solution for the following types of users:

• Non-ML experts such as business analysts, data engineers, or developers, who are domain experts and are interested in low-code no-code (LCNC) tools to guide them in preparing data for ML and building ML models. This persona typically is only a SageMaker Canvas user and often relies on ML experts in their organization to review and approve their work.
• ML experts who are interested in how LCNC tools can accelerate parts of the ML lifecycle (such as data prep), but are also likely to take a high-code approach to certain parts of the ML lifecycle (such as model building). This persona is typically a SageMaker Studio user who might also be a SageMaker Canvas user. ML experts also often play a role in reviewing and approving the work of non-ML experts for production use cases.

The utility of the solutions proposed in this post is two-fold. Firstly, by demonstrating how you can share models across SageMaker Canvas and SageMaker Studio, non-ML and ML experts can collaborate across their preferred environments, which might be a no-code environment (SageMaker Canvas) for non-experts and a high-code environment (SageMaker Studio) for experts. Secondly, by demonstrating how to share a model from SageMaker Canvas to SageMaker Studio, we show how ML experts who want to pivot from a LCNC approach for development to a high-code approach for production can do so across SageMaker environments. The solution outlined in this post is for users of the new SageMaker Studio. For users of SageMaker Studio Classic, see Collaborate with data scientists for how you can seamlessly transition between SageMaker Canvas and SageMaker Studio Classic.

Solution overview

To seamlessly transition between no-code and code-first ML with SageMaker Canvas and SageMaker Studio, we have outlined two options. You can choose the option based on your requirements. In some cases, you might decide to use both options in parallel.

• Option 1: SageMaker Model Registry – A SageMaker Canvas user registers their model in the Amazon SageMaker Model Registry, invoking a governance workflow for ML experts to review model details and metrics, then approve or reject it, after which the user can deploy the approved model from SageMaker Canvas. This option is an automated sharing process providing you with built-in governance and approval tracking. You can view the model metrics; however, there is limited visibility on the model code and architecture. The following diagram illustrates the architecture.

• Option 2: Notebook export – In this option, the SageMaker Canvas user exports the full notebook from SageMaker Canvas to Amazon Simple Storage Service (Amazon S3), then shares it with ML experts to import into SageMaker Studio, enabling complete visibility and customization of the model code and logic before the ML expert deploys the enhanced model. In this option, there is complete visibility of the model code and architecture with the ability for the ML expert to customize and enhance the model in SageMaker Studio. However, this option demands a manual export and import of the model notebook into the IDE. The following diagram illustrates this architecture.

The following phases describe the steps for collaboration:

• Share – The SageMaker Canvas user registers the model from SageMaker Canvas or downloads the notebook from SageMaker Canvas
• Review – The SageMaker Studio user accesses the model through the model registry to review and run the exported notebook through JupyterLab to validate the model
• Approval – The SageMaker Studio user approves the model from the model registry
• Deploy – The SageMaker Studio user can deploy the model from JupyterLab, or the SageMaker Canvas user can deploy the model from SageMaker Canvas

Let’s look at the two options (model registry and notebook export) within each step in detail.

Prerequisites

Before you dive into the solution, make sure you have signed up for and created an AWS account. Then you need to create an administrative user and a group. For instructions on both steps, refer to Set Up Amazon SageMaker Prerequisites. You can skip this step if you already have your own version of SageMaker Studio running.

Complete the prerequisites for setting up SageMaker Canvas and create the model of your choice for your use case.

Share the model

The SageMaker Canvas user shares the model with the SageMaker Studio user by either registering it in SageMaker Model Registry, which triggers a governance workflow, or by downloading the full notebook from SageMaker Canvas and providing it to the SageMaker Studio user.

SageMaker Model Registry

To deploy using SageMaker Model Registry, complete the following steps:

1. After a model is created in SageMaker Canvas, choose the options menu (three vertical dots) and choose Add to Model Registry.
   
2. Enter a name for the model group.
3. Choose Add.
   

You can now see the model is registered.

You can also see the model is pending approval.

SageMaker notebook export

To deploy using a SageMaker notebook, complete the following steps:

1. On the options menu, choose View Notebook.
   
2. Choose Copy S3 URI.
   

You can now share the S3 URI with the SageMaker Studio user.

Review the model

The SageMaker Studio user accesses the shared model through the model registry to review its details and metrics, or they can import the exported notebook into SageMaker Studio and use Jupyter notebooks to thoroughly validate the model’s code, logic, and performance.

SageMaker Model Registry

To use the model registry, complete the following steps:

1. On the SageMaker Studio console, choose Models in the navigation pane.
2. Choose Registered models.
3. Choose your model.
   

You can review the model details and see that the status is pending.

You can also review the different metrics to check on the model performance.

You can view the model metrics; however, there is limited visibility on the model code and architecture. If you want complete visibility of the model code and architecture with the ability to customize and enhance the model, use the notebook export option.

SageMaker notebook export

To use the notebook export option as the SageMaker Studio user, complete the following steps.

1. Launch SageMaker Studio and choose JupyterLab under Applications.
2. Open the JupyterLab space.If you don’t have a JupyterLab space, you can create one.
   
3. Open a terminal and run the following command to copy the notebook from Amazon S3 to SageMaker Studio (the account number in the following example is changed to awsaccountnumber):

   sagemaker-user@default:~$ aws s3 cp s3://sagemaker-us-east-1-awsaccountnumber/Canvas/default-20240130t161835/Training/output/Canvas1707947728560/sagemaker-automl-candidates/notebooks/SageMakerAutopilotCandidateDefinitionNotebook.ipynb ./canvas.ipynb

   

4. After the notebook is downloaded, you can open the notebook and run the notebook to evaluate further.

Approve the model

After a comprehensive review, the SageMaker Studio user can make an informed decision to either approve or reject the model in the model registry based on their assessment of its quality, accuracy, and suitability for the intended use case.

For users who registered their model via the Canvas UI, please follow the below steps to approve the model. For users who exported the model notebook from the Canvas UI, you may register and approve the model using SageMaker model registry, however, these steps are not required.

SageMaker Model Registry

As the SageMaker Studio user, when you’re comfortable with the model, you can update the status to approved. Approval happens only in SageMaker Model Registry. Complete the following steps:

1. In SageMaker Studio, navigate to the version of the model.
2. On the options menu, choose Update status and Approved.
   
3. Enter an optional comment and choose Save and update.
   

Now you can see the model is approved.

Deploy the model

Once the model is ready to deploy (it has received necessary reviews and approvals), users have two options. For users who took the model registry approach, they can deploy from either SageMaker Studio or from SageMaker Canvas. For users who took the model notebook export approach, they can deploy from SageMaker Studio. Both deployment options are detailed below.

Deploy via SageMaker Studio

The SageMaker Studio user can deploy the model from the JupyterLab space.

After the model is deployed, you can navigate to the SageMaker console, choose Endpoints under Inference in the navigation pane, and view the model.

Deploy via SageMaker Canvas

Alternatively, if the deployment is handled by the SageMaker Canvas user, you can deploy the model from SageMaker Canvas.

After the model is deployed, you can navigate to the Endpoints page on the SageMaker console to view the model.

Clean up

To avoid incurring future session charges, log out of SageMaker Canvas.

To avoid ongoing charges, delete the SageMaker inference endpoints. You can delete the endpoints via the SageMaker console or from the SageMaker Studio notebook using the following commands:

predictor.delete_model()

predictor.delete_endpoint()

Conclusion

Previously, you could only share models to SageMaker Canvas (or view shared SageMaker Canvas models) in SageMaker Studio Classic. In this post, we showed how to share models built in SageMaker Canvas with SageMaker Studio so that different teams can collaborate and you can pivot from a no-code to a high-code deployment path. By either using SageMaker Model Registry or exporting notebooks, ML experts and non-experts can collaborate, review, and enhance models across these platforms, enabling a smooth workflow from data preparation to production deployment.

For more information about collaborating on models using SageMaker Canvas, refer to Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and Amazon SageMaker Canvas.

About the Authors

Rajakumar Sampathkumar is a Principal Technical Account Manager at AWS, providing customer guidance on business-technology alignment and supporting the reinvention of their cloud operation models and processes. He is passionate about cloud and machine learning. Raj is also a machine learning specialist and works with AWS customers to design, deploy, and manage their AWS workloads and architectures.

Meenakshisundaram Thandavarayan works for AWS as an AI/ ML Specialist. He has a passion to design, create, and promote human-centered data and analytics experiences. Meena focusses on developing sustainable systems that deliver measurable, competitive advantages for strategic customers of AWS. Meena is a connector and design thinker, and strives to drive business to new ways of working through innovation, incubation, and democratization.

Claire O’Brien Rajkumar is a Sr. Product Manager on the Amazon SageMaker team focused on SageMaker Canvas, the SageMaker low-code no-code workspace for ML and generative AI. SageMaker Canvas helps democratize ML and generative AI by lowering barriers to adoption for those new to ML and accelerating workflows for advanced practitioners.

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The rise of contextual and semantic search has made ecommerce and retail businesses search straightforward for its consumers. Search engines and recommendation systems powered by generative AI can improve the product search experience exponentially by understanding natural language queries and returning more accurate results. This enhances the overall user experience, helping customers find exactly what they’re looking for.

Amazon OpenSearch Service now supports the cosine similarity metric for k-NN indexes. Cosine similarity measures the cosine of the angle between two vectors, where a smaller cosine angle denotes a higher similarity between the vectors. With cosine similarity, you can measure the orientation between two vectors, which makes it a good choice for some specific semantic search applications.

In this post, we show how to build a contextual text and image search engine for product recommendations using the Amazon Titan Multimodal Embeddings model, available in Amazon Bedrock, with Amazon OpenSearch Serverless.

A multimodal embeddings model is designed to learn joint representations of different modalities like text, images, and audio. By training on large-scale datasets containing images and their corresponding captions, a multimodal embeddings model learns to embed images and texts into a shared latent space. The following is a high-level overview of how it works conceptually:

• Separate encoders – These models have separate encoders for each modality—a text encoder for text (for example, BERT or RoBERTa), image encoder for images (for example, CNN for images), and audio encoders for audio (for example, models like Wav2Vec). Each encoder generates embeddings capturing semantic features of their respective modalities
• Modality fusion – The embeddings from the uni-modal encoders are combined using additional neural network layers. The goal is to learn interactions and correlations between the modalities. Common fusion approaches include concatenation, element-wise operations, pooling, and attention mechanisms.
• Shared representation space – The fusion layers help project the individual modalities into a shared representation space. By training on multimodal datasets, the model learns a common embedding space where embeddings from each modality that represent the same underlying semantic content are closer together.
• Downstream tasks – The joint multimodal embeddings generated can then be used for various downstream tasks like multimodal retrieval, classification, or translation. The model uses correlations across modalities to improve performance on these tasks compared to individual modal embeddings. The key advantage is the ability to understand interactions and semantics between modalities like text, images, and audio through joint modeling.

Solution overview

The solution provides an implementation for building a large language model (LLM) powered search engine prototype to retrieve and recommend products based on text or image queries. We detail the steps to use an Amazon Titan Multimodal Embeddings model to encode images and text into embeddings, ingest embeddings into an OpenSearch Service index, and query the index using the OpenSearch Service k-nearest neighbors (k-NN) functionality.

This solution includes the following components:

• Amazon Titan Multimodal Embeddings model – This foundation model (FM) generates embeddings of the product images used in this post. With Amazon Titan Multimodal Embeddings, you can generate embeddings for your content and store them in a vector database. When an end-user submits any combination of text and image as a search query, the model generates embeddings for the search query and matches them to the stored embeddings to provide relevant search and recommendations results to end-users. You can further customize the model to enhance its understanding of your unique content and provide more meaningful results using image-text pairs for fine-tuning. By default, the model generates vectors (embeddings) of 1,024 dimensions, and is accessed via Amazon Bedrock. You can also generate smaller dimensions to optimize for speed and performance
• Amazon OpenSearch Serverless – It is an on-demand serverless configuration for OpenSearch Service. We use Amazon OpenSearch Serverless as a vector database for storing embeddings generated by the Amazon Titan Multimodal Embeddings model. An index created in the Amazon OpenSearch Serverless collection serves as the vector store for our Retrieval Augmented Generation (RAG) solution.
• Amazon SageMaker Studio – It is an integrated development environment (IDE) for machine learning (ML). ML practitioners can perform all ML development steps—from preparing your data to building, training, and deploying ML models.

The solution design consists of two parts: data indexing and contextual search. During data indexing, you process the product images to generate embeddings for these images and then populate the vector data store. These steps are completed prior to the user interaction steps.

In the contextual search phase, a search query (text or image) from the user is converted into embeddings and a similarity search is run on the vector database to find the similar product images based on similarity search. You then display the top similar results. All the code for this post is available in the GitHub repo.

The following diagram illustrates the solution architecture.

The following are the solution workflow steps:

1. Download the product description text and images from the public Amazon Simple Storage Service (Amazon S3) bucket.
2. Review and prepare the dataset.
3. Generate embeddings for the product images using the Amazon Titan Multimodal Embeddings model (amazon.titan-embed-image-v1). If you have a huge number of images and descriptions, you can optionally use the Batch inference for Amazon Bedrock.
4. Store embeddings into the Amazon OpenSearch Serverless as the search engine.
5. Finally, fetch the user query in natural language, convert it into embeddings using the Amazon Titan Multimodal Embeddings model, and perform a k-NN search to get the relevant search results.

We use SageMaker Studio (not shown in the diagram) as the IDE to develop the solution.

These steps are discussed in detail in the following sections. We also include screenshots and details of the output.

Prerequisites

To implement the solution provided in this post, you should have the following:

• An AWS account and familiarity with FMs, Amazon Bedrock, Amazon SageMaker, and OpenSearch Service.
• The Amazon Titan Multimodal Embeddings model enabled in Amazon Bedrock. You can confirm it’s enabled on the Model access page of the Amazon Bedrock console. If Amazon Titan Multimodal Embeddings is enabled, the access status will show as Access granted, as shown in the following screenshot.

If the model is not available, enable access to the model by choosing Manage model access, selecting Amazon Titan Multimodal Embeddings G1, and choosing Request model access. The model is enabled for use immediately.

• You also need a SageMaker Studio domain. If you don’t have a SageMaker Studio domain already configured, refer to Amazon SageMaker simplifies the Amazon SageMaker Studio setup for individual users for steps to create one.

Set up the solution

When the prerequisite steps are complete, you’re ready to set up the solution:

1. In your AWS account, open the SageMaker console and choose Studio in the navigation pane.
2. Choose your domain and user profile, then choose Open Studio.

Your domain and user profile name may be different.

3. Choose System terminal under Utilities and files.
4. Run the following command to clone the GitHub repo to the SageMaker Studio instance:

git clone https://github.com/aws-samples/amazon-bedrock-samples.git

5. Navigate to the multimodal/Titan/titan-multimodal-embeddings/amazon-bedrock-multimodal-oss-searchengine-e2e folder.
6. Open the titan_mm_embed_search_blog.ipynb notebook.

Run the solution

Open the file titan_mm_embed_search_blog.ipynb and use the Data Science Python 3 kernel. On the Run menu, choose Run All Cells to run the code in this notebook.

This notebook performs the following steps:

1. Install the packages and libraries required for this solution.
2. Load the publicly available Amazon Berkeley Objects Dataset and metadata in a pandas data frame.

The dataset is a collection of 147,702 product listings with multilingual metadata and 398,212 unique catalogue images. For this post, you only use the item images and item names in US English. You use approximately 1,600 products.

3. Generate embeddings for the item images using the Amazon Titan Multimodal Embeddings model using the get_titan_multomodal_embedding() function. For the sake of abstraction, we have defined all important functions used in this notebook in the utils.py file.

Next, you create and set up an Amazon OpenSearch Serverless vector store (collection and index).

4. Before you create the new vector search collection and index, you must first create three associated OpenSearch Service policies: the encryption security policy, network security policy, and data access policy.

5. Finally, ingest the image embedding into the vector index.

Now you can perform a real-time multimodal search.

Run a contextual search

In this section, we show the results of contextual search based on a text or image query.

First, let’s perform an image search based on text input. In the following example, we use the text input “drinkware glass” and send it to the search engine to find similar items.

The following screenshot shows the results.

Now let’s look at the results based on a simple image. The input image gets converted into vector embeddings and, based on the similarity search, the model returns the result.

You can use any image, but for the following example, we use a random image from the dataset based on item ID (for example, item_id = “B07JCDQWM6”), and then send this image to the search engine to find similar items.

The following screenshot shows the results.

Clean up

To avoid incurring future charges, delete the resources used in this solution. You can do this by running the cleanup section of the notebook.

Conclusion

This post presented a walkthrough of using the Amazon Titan Multimodal Embeddings model in Amazon Bedrock to build powerful contextual search applications. In particular, we demonstrated an example of a product listing search application. We saw how the embeddings model enables efficient and accurate discovery of information from images and textual data, thereby enhancing the user experience while searching for the relevant items.

Amazon Titan Multimodal Embeddings helps you power more accurate and contextually relevant multimodal search, recommendation, and personalization experiences for end-users. For example, a stock photography company with hundreds of millions of images can use the model to power its search functionality, so users can search for images using a phrase, image, or a combination of image and text.

The Amazon Titan Multimodal Embeddings model in Amazon Bedrock is now available in the US East (N. Virginia) and US West (Oregon) AWS Regions. To learn more, refer to Amazon Titan Image Generator, Multimodal Embeddings, and Text models are now available in Amazon Bedrock, the Amazon Titan product page, and the Amazon Bedrock User Guide. To get started with Amazon Titan Multimodal Embeddings in Amazon Bedrock, visit the Amazon Bedrock console.

Start building with the Amazon Titan Multimodal Embeddings model in Amazon Bedrock today.

About the Authors

Sandeep Singh is a Senior Generative AI Data Scientist at Amazon Web Services, helping businesses innovate with generative AI. He specializes in Generative AI, Artificial Intelligence, Machine Learning, and System Design. He is passionate about developing state-of-the-art AI/ML-powered solutions to solve complex business problems for diverse industries, optimizing efficiency and scalability.

Mani Khanuja is a Tech Lead – Generative AI Specialists, author of the book Applied Machine Learning and High Performance Computing on AWS, and a member of the Board of Directors for Women in Manufacturing Education Foundation Board. She leads machine learning projects in various domains such as computer vision, natural language processing, and generative AI. She speaks at internal and external conferences such AWS re:Invent, Women in Manufacturing West, YouTube webinars, and GHC 23. In her free time, she likes to go for long runs along the beach.

Rupinder Grewal is a Senior AI/ML Specialist Solutions Architect with AWS. He currently focuses on serving of models and MLOps on Amazon SageMaker. Prior to this role, he worked as a Machine Learning Engineer building and hosting models. Outside of work, he enjoys playing tennis and biking on mountain trails.

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The generative artificial intelligence (AI) revolution is in full swing, and customers of all sizes and across industries are taking advantage of this transformative technology to reshape their businesses. From reimagining workflows to make them more intuitive and easier to enhancing decision-making processes through rapid information synthesis, generative AI promises to redefine how we interact with machines. It’s been amazing to see the number of companies launching innovative generative AI applications on AWS using Amazon Bedrock. Siemens is integrating Amazon Bedrock into its low-code development platform Mendix to allow thousands of companies across multiple industries to create and upgrade applications with the power of generative AI. Accenture and Anthropic are collaborating with AWS to help organizations—especially those in highly-regulated industries like healthcare, public sector, banking, and insurance—responsibly adopt and scale generative AI technology with Amazon Bedrock. This collaboration will help organizations like the District of Columbia Department of Health speed innovation, improve customer service, and improve productivity, while keeping data private and secure. Amazon Pharmacy is using generative AI to fill prescriptions with speed and accuracy, making customer service faster and more helpful, and making sure that the right quantities of medications are stocked for customers.

To power so many diverse applications, we recognized the need for model diversity and choice for generative AI early on. We know that different models excel in different areas, each with unique strengths tailored to specific use cases, leading us to provide customers with access to multiple state-of-the-art large language models (LLMs) and foundation models (FMs) through a unified service: Amazon Bedrock. By facilitating access to top models from Amazon, Anthropic, AI21 Labs, Cohere, Meta, Mistral AI, and Stability AI, we empower customers to experiment, evaluate, and ultimately select the model that delivers optimal performance for their needs.

Announcing Mistral Large on Amazon Bedrock

Today, we are excited to announce the next step on this journey with an expanded collaboration with Mistral AI. A French startup, Mistral AI has quickly established itself as a pioneering force in the generative AI landscape, known for its focus on portability, transparency, and its cost-effective design requiring fewer computational resources to run. We recently announced the availability of Mistral 7B and Mixtral 8x7B models on Amazon Bedrock, with weights that customers can inspect and modify. Today, Mistral AI is bringing its latest and most capable model, Mistral Large, to Amazon Bedrock, and is committed to making future models accessible to AWS customers. Mistral AI will also use AWS AI-optimized AWS Trainium and AWS Inferentia to build and deploy its future foundation models on Amazon Bedrock, benefitting from the price, performance, scale, and security of AWS. Along with this announcement, starting today, customers can use Amazon Bedrock in the AWS Europe (Paris) Region. At launch, customers will have access to some of the latest models from Amazon, Anthropic, Cohere, and Mistral AI, expanding their options to support various use cases from text understanding to complex reasoning.

Mistral Large boasts exceptional language understanding and generation capabilities, which is ideal for complex tasks that require reasoning capabilities or ones that are highly specialized, such as synthetic text generation, code generation, Retrieval Augmented Generation (RAG), or agents. For example, customers can build AI agents capable of engaging in articulate conversations, generating nuanced content, and tackling complex reasoning tasks. The model’s strengths also extend to coding, with proficiency in code generation, review, and comments across mainstream coding languages. And Mistral Large’s exceptional multilingual performance, spanning French, German, Spanish, and Italian, in addition to English, presents a compelling opportunity for customers. By offering a model with robust multilingual support, AWS can better serve customers with diverse language needs, fostering global accessibility and inclusivity for generative AI solutions.

By integrating Mistral Large into Amazon Bedrock, we can offer customers an even broader range of top-performing LLMs to choose from. No single model is optimized for every use case, and to unlock the value of generative AI, customers need access to a variety of models to discover what works best based for their business needs. We are committed to continuously introducing the best models, providing customers with access to the latest and most innovative generative AI capabilities.

“We are excited to announce our collaboration with AWS to accelerate the adoption of our frontier AI technology with organizations around the world. Our mission is to make frontier AI ubiquitous, and to achieve this mission, we want to collaborate with the world’s leading cloud provider to distribute our top-tier models. We have a long and deep relationship with AWS and through strengthening this relationship today, we will be able to provide tailor-made AI to builders around the world.”

– Arthur Mensch, CEO at Mistral AI.

Customers appreciate choice

Since we first announced Amazon Bedrock, we have been innovating at a rapid clip—adding more powerful features like agents and guardrails. And we’ve said all along that more exciting innovations, including new models will keep coming. With more model choice, customers tell us they can achieve remarkable results:

“The ease of accessing different models from one API is one of the strengths of Bedrock. The model choices available have been exciting. As new models become available, our AI team is able to quickly and easily evaluate models to know if they fit our needs. The security and privacy that Bedrock provides makes it a great choice to use for our AI needs.”

– Jamie Caramanica, SVP, Engineering at CS Disco.

“Our top priority today is to help organizations use generative AI to support employees and enhance bots through a range of applications, such as stronger topic, sentiment, and tone detection from customer conversations, language translation, content creation and variation, knowledge optimization, answer highlighting, and auto summarization. To make it easier for them to tap into the potential of generative AI, we’re enabling our users with access to a variety of large language models, such as Genesys-developed models and multiple third-party foundational models through Amazon Bedrock, including Anthropic’s Claude, AI21 Labs’s Jurrassic-2, and Amazon Titan. Together with AWS, we’re offering customers exponential power to create differentiated experiences built around the needs of their business, while helping them prepare for the future.”

– Glenn Nethercutt, CTO at Genesys.

As the generative AI revolution continues to unfold, AWS is poised to shape its future, empowering customers across industries to drive innovation, streamline processes, and redefine how we interact with machines. Together with outstanding partners like Mistral AI, and with Amazon Bedrock as the foundation, our customers can build more innovative generative AI applications.

Democratizing access to LLMs and FMs

Amazon Bedrock is democratizing access to cutting-edge LLMs and FMs and AWS is the only cloud provider to offer the most popular and advanced FMs to customers. The collaboration with Mistral AI represents a significant milestone in this journey, further expanding Amazon Bedrock’s diverse model offerings and reinforcing our commitment to empowering customers with unparalleled choice through Amazon Bedrock. By recognizing that no single model can optimally serve every use case, AWS has paved the way for customers to unlock the full potential of generative AI. Through Amazon Bedrock, organizations can experiment with and take advantage of the unique strengths of multiple top-performing models, tailoring their solutions to specific needs, industry domains, and workloads. This unprecedented choice, combined with the robust security, privacy, and scalability of AWS, enables customers to harness the power of generative AI responsibly and with confidence, no matter their industry or regulatory constraints.

Resources

1. Mistral Large News Blog
2. About Amazon Blog
3. Mistral AI on Amazon Bedrock Product Page

About the author

Swami Sivasubramanian is Vice President of Data and Machine Learning at AWS. In this role, Swami oversees all AWS Database, Analytics, and AI & Machine Learning services. His team’s mission is to help organizations put their data to work with a complete, end-to-end data solution to store, access, analyze, and visualize, and predict.

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In 2023, AWS announced an expanded collaboration with Hugging Face to accelerate our customers’ generative artificial intelligence (AI) journey. Hugging Face, founded in 2016, is the premier AI platform with over 500,000 open source models and more than 100,000 datasets. Over the past year, we have partnered to make it effortless to train, fine-tune, and deploy Hugging Face models using Amazon SageMaker, AWS Trainium, and AWS Inferentia. Developers using Hugging Face can now optimize performance and lower cost to bring generative AI applications to production faster.

We are happy to announce a generative AI roadshow in North America where you can meet with Hugging Face and AWS experts, learn about the latest developments in generative AI, and get hands-on experience with fine-tuning and deploying foundation models. As part of this roadshow, Julien Simon, Chief Evangelist at Hugging Face, will travel to eight AWS headquarters across North America between April and June. You can meet with Julien and AWS experts to dive deep into your use cases and learn how AWS and Hugging Face can help.

The following are the cities and dates for the 2024 roadshow:

• Seattle, WA: April 9–11
• San Francisco, CA: April 12
• Santa Clara, CA: April 15, April 17
• Los Angeles, CA: April 18–19
• Boston, MA: April 22–23
• New York City, NY: April 24–26
• Austin, TX: May 28–31
• Arlington, Washington DC: June 3–5

You can participate in the roadshow in two ways:

• Request a 1:1 meeting with Julien Simon and AWS experts. Reach out to your AWS Account Manager or submit your request.
• Register for an in-person, hands-on developer workshop in one of the following four cities, to learn how to deploy open source models from Hugging Face to build generative AI applications while reducing production costs with SageMaker and AWS Inferentia2:
  • Register for Seattle
  • Register for Santa Clara
  • Register for NYC
  • Register for Austin

For further inquiries, reach out to the AMER Hugging Face roadshow organizers.

We look forward to seeing you there. To learn more about the AWS collaboration with Hugging Face, visit the Amazon SageMaker resources and AWS Inferentia and Trainium space on the Hugging Face website.

About the authors

Shruti Koparkar is a Senior Product Marketing Manager at AWS. She helps customers explore, evaluate, and adopt Amazon EC2 accelerated computing infrastructure for their machine learning needs.

Hoko Hongo is a Senior GTM Specialist at AWS, supporting go-to-market and customer acceleration programs to further the customer’s generative AI journey.

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This is a guest post co-written with Michael Feil at Gradient.

Evaluating the performance of large language models (LLMs) is an important step of the pre-training and fine-tuning process before deployment. The faster and more frequent you’re able to validate performance, the higher the chances you’ll be able to improve the performance of the model.

At Gradient, we work on custom LLM development, and just recently launched our AI Development Lab, offering enterprise organizations a personalized, end-to-end development service to build private, custom LLMs and artificial intelligence (AI) co-pilots. As part of this process, we regularly evaluate the performance of our models (tuned, trained, and open) against open and proprietary benchmarks. While working with the AWS team to train our models on AWS Trainium, we realized we were restricted to both VRAM and the availability of GPU instances when it came to the mainstream tool for LLM evaluation, lm-evaluation-harness. This open source framework lets you score different generative language models across various evaluation tasks and benchmarks. It is used by leaderboards such as Hugging Face for public benchmarking.

To overcome these challenges, we decided to build and open source our solution—integrating AWS Neuron, the library behind AWS Inferentia and Trainium, into lm-evaluation-harness. This integration made it possible to benchmark v-alpha-tross, an early version of our Albatross model, against other public models during the training process and after.

For context, this integration runs as a new model class within lm-evaluation-harness, abstracting the inference of tokens and log-likelihood estimation of sequences without affecting the actual evaluation task. The decision to move our internal testing pipeline to Amazon Elastic Compute Cloud (Amazon EC2) Inf2 instances (powered by AWS Inferentia2) enabled us to access up to 384 GB of shared accelerator memory, effortlessly fitting all of our current public architectures. By using AWS Spot Instances, we were able to take advantage of unused EC2 capacity in the AWS Cloud—enabling cost savings up to 90% discounted from on-demand prices. This minimized the time it took for testing and allowed us to test more frequently because we were able to test across multiple instances that were readily available and release the instances when we were finished.

In this post, we give a detailed breakdown of our tests, the challenges that we encountered, and an example of using the testing harness on AWS Inferentia.

Benchmarking on AWS Inferentia2

The goal of this project was to generate identical scores as shown in the Open LLM Leaderboard (for many CausalLM models available on Hugging Face), while retaining the flexibility to run it against private benchmarks. To see more examples of available models, see AWS Inferentia and Trainium on Hugging Face.

The code changes required to port over a model from Hugging Face transformers to the Hugging Face Optimum Neuron Python library were quite low. Because lm-evaluation-harness uses AutoModelForCausalLM, there is a drop in replacement using NeuronModelForCausalLM. Without a precompiled model, the model is automatically compiled in the moment, which could add 15–60 minutes onto a job. This gave us the flexibility to deploy testing for any AWS Inferentia2 instance and supported CausalLM model.

Results

Because of the way the benchmarks and models work, we didn’t expect the scores to match exactly across different runs. However, they should be very close based on the standard deviation, and we have consistently seen that, as shown in the following table. The initial benchmarks we ran on AWS Inferentia2 were all confirmed by the Hugging Face leaderboard.

In lm-evaluation-harness, there are two main streams used by different tests: generate_until and loglikelihood. The gsm8k test primarily uses generate_until to generate responses just like during inference. Loglikelihood is mainly used in benchmarking and testing, and examines the probability of different outputs being produced. Both work in Neuron, but the loglikelihood method in SDK 2.16 uses additional steps to determine the probabilities and can take extra time.

Get started with Neuron and lm-evaluation-harness

The code in this section can help you use lm-evaluation-harness and run it against supported models on Hugging Face. To see some available models, visit AWS Inferentia and Trainium on Hugging Face.

If you’re familiar with running models on AWS Inferentia2, you might notice that there is no num_cores setting passed in. Our code detects how many cores are available and automatically passes that number in as a parameter. This lets you run the test using the same code regardless of what instance size you are using. You might also notice that we are referencing the original model, not a Neuron compiled version. The harness automatically compiles the model for you as needed.

The following steps show you how to deploy the Gradient gradientai/v-alpha-tross model we tested. If you want to test with a smaller example on a smaller instance, you can use the mistralai/Mistral-7B-v0.1 model.

1. The default quota for running On-Demand Inf instances is 0, so you should request an increase via Service Quotas. Add another request for all Inf Spot Instance requests so you can test with Spot Instances. You will need a quota of 192 vCPUs for this example using an inf2.48xlarge instance, or a quota of 4 vCPUs for a basic inf2.xlarge (if you are deploying the Mistral model). Quotas are AWS Region specific, so make sure you request in us-east-1 or us-west-2.
2. Decide on your instance based on your model. Because v-alpha-tross is a 70B architecture, we decided use an inf2.48xlarge instance. Deploy an inf2.xlarge (for the 7B Mistral model). If you are testing a different model, you may need to adjust your instance depending on the size of your model.
3. Deploy the instance using the Hugging Face DLAMI version 20240123, so that all the necessary drivers are installed. (The price shown includes the instance cost and there is no additional software charge.)
4. Adjust the drive size to 600 GB (100 GB for Mistral 7B).
5. Clone and install lm-evaluation-harness on the instance. We specify a build so that we know any variance is due to model changes, not test or code changes.

git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
# optional: pick specific revision from the main branch version to reproduce the exact results
git checkout 756eeb6f0aee59fc624c81dcb0e334c1263d80e3
# install the repository without overwriting the existing torch and torch-neuronx installation
pip install --no-deps -e . 
pip install peft evaluate jsonlines numexpr pybind11 pytablewriter rouge-score sacrebleu sqlitedict tqdm-multiprocess zstandard hf_transfer

6. Run lm_eval with the hf-neuron model type and make sure you have a link to the path back to the model on Hugging Face:

# e.g use mistralai/Mistral-7B-v0.1 if you are on inf2.xlarge
MODEL_ID=gradientai/v-alpha-tross

python -m lm_eval --model "neuronx" --model_args "pretrained=$MODEL_ID,dtype=bfloat16" --batch_size 1 --tasks gsm8k

If you run the preceding example with Mistral, you should receive the following output (on the smaller inf2.xlarge, it could take 250 minutes to run):

███████████████████████| 1319/1319 [32:52<00:00,  1.50s/it]
neuronx (pretrained=mistralai/Mistral-7B-v0.1,dtype=bfloat16), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|Tasks|Version|  Filter  |n-shot|  Metric   |Value |   |Stderr|
|-----|------:|----------|-----:|-----------|-----:|---|-----:|
|gsm8k|      2|get-answer|     5|exact_match|0.3806|±  |0.0134|

Clean up

When you are done, be sure to stop the EC2 instances via the Amazon EC2 console.

Conclusion

The Gradient and Neuron teams are excited to see a broader adoption of LLM evaluation with this release. Try it out yourself and run the most popular evaluation framework on AWS Inferentia2 instances. You can now benefit from the on-demand availability of AWS Inferentia2 when you’re using custom LLM development from Gradient. Get started hosting models on AWS Inferentia with these tutorials.

About the Authors

Michael Feil is an AI engineer at Gradient and previously worked as a ML engineer at Rodhe & Schwarz and a researcher at Max-Plank Institute for Intelligent Systems and Bosch Rexroth. Michael is a leading contributor to various open source inference libraries for LLMs and open source projects such as StarCoder. Michael holds a bachelor’s degree in mechatronics and IT from KIT and a master’s degree in robotics from Technical University of Munich.

Jim Burtoft is a Senior Startup Solutions Architect at AWS and works directly with startups like Gradient. Jim is a CISSP, part of the AWS AI/ML Technical Field Community, a Neuron Ambassador, and works with the open source community to enable the use of Inferentia and Trainium. Jim holds a bachelor’s degree in mathematics from Carnegie Mellon University and a master’s degree in economics from the University of Virginia.

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Amazon SageMaker Canvas allows you to use machine learning (ML) to generate predictions without having to write any code. It does so by covering the end-to-end ML workflow: whether you’re looking for powerful data preparation and AutoML, managed endpoint deployment, simplified MLOps capabilities, or the ability to configure foundation models for generative AI, SageMaker Canvas can help you achieve your goals.

To enable agility for your users while ensuring secure environments, you can adopt single sign-on (SSO) using AWS IAM Identity Center, which is the recommended AWS service for managing user access to AWS resources. With IAM Identity Center, you can create or connect workforce users and centrally manage their access across all their AWS accounts and applications.

Part 1 of this series describes the necessary steps to configure SSO for SageMaker Canvas using IAM Identity Center for Amazon SageMaker Studio Classic.

In this post, we walk you through the necessary steps to configure SSO for SageMaker Canvas using IAM Identity Center for the updated Amazon SageMaker Studio. Your users can seamlessly access SageMaker Canvas with their credentials from IAM Identity Center without having to first go through the AWS Management Console. We also demonstrate how you can streamline user management with IAM Identity Center.

Solution overview

To configure SSO from IAM Identity Center, you need to complete the following steps:

1. Enable IAM Identity Center using AWS Organizations
2. Create a SageMaker Studio domain that uses IAM Identity Center for user authentication
3. Create users or groups in IAM Identity Center
4. Add users or groups to the SageMaker Studio domain

We will also show how to rename the SageMaker Studio application to clearly identify it as SageMaker Canvas, and how to access it using IAM Identity Center.

Enable IAM Identity Center

Follow these steps to connect SageMaker Canvas to IAM Identity Center:

1. On the IAM Identity Center console, choose Enable.
2. Choose Enable with AWS Organizations.
   
3. Choose Edit to add an instance name.
   
4. Enter a name for your instance (for this post, canvas-app).
5. Choose Save changes.

Create the SageMaker Studio domain

In this section, we create SageMaker Studio domain and configure the authentication method as IAM Identity Center. Complete the following steps:

1. On the SageMaker console, choose Domains.
2. Choose Create domain.
   
3. Choose Set up for organizations.
4. Choose Set up.
   
5. Enter a domain name of your choice (for this post, canvas-domain).
6. Choose Next.
   
7. Select AWS Identity Center.
   
8. Choose Create a new role.
9. Select the SageMaker Canvas permissions that you want to grant.

For more details about permissions, see Users and ML Activities.

10. Specify one or more Amazon Simple Storage Service (Amazon S3) bucket.
11. Choose Next.
    
12. Select SageMaker Studio – New.
    
13. Choose Next.
    

Next, you can provide VPC details for your network configuration.

14. For this post, we select Public internet access.
    
15. Choose your VPC, subnets, and security groups.
16. Choose Next.
    
17. Keep default storage configuration and choose Next.
    
18. Choose Submit.
    

Wait for SageMaker domain status to change to InService.

Rename the SageMaker Studio application

Before we create a user, let’s rename the SageMaker Studio application name. This will allow users to quickly identify the SageMaker Canvas application when they log in through IAM Identity Center, where they may have access to multiple applications.

1. On the IAM Identity Center console, choose Applications.
2. Choose the SageMaker Studio application on the AWS managed tab.
   
3. Choose Edit details on the Actions menu.
   
4. For Display name, enter a name (for this post, Canvas).
5. For Description, enter a description.
6. Choose Save changes.
   

Create a user in IAM Identity Center

Now you can create users, and optionally, groups, that will be given access to SageMaker Canvas. For this post, we create a single user to demonstrate the process to provide access. However, groups are typically preferred for better user management, and to provision access in organizations.

A user group is a collection of users. Groups let you specify permissions for multiple users, which can make it more straightforward to manage the permissions for those users. For example, you could have a user group called business analysts and give that user group permission to SageMaker Canvas; all users in that group will have SageMaker Canvas access. If a new user joins your organization and needs access to SageMaker Canvas, you can add the user to the business analyst group. If a person changes jobs in your organization, instead of editing that user’s permissions, you can remove them from the old user groups and add them to the appropriate new user groups.

Complete the following steps to create a user in IAM Identity Center to test the SageMaker Canvas application access:

1. On the IAM Identity Center console, choose Users in the navigation pane.
2. Choose Add user.
   
3. Provide required details such as the user name, email address, first name, and last name.
   
4. Choose Next.
   
5. Choose Add user.
   

You see a success message that the user has been added successfully.

Add users to the SageMaker Studio domain

You need to add this user to the SageMaker domain you created. If you’re using groups, then you add the group, not just a single user.

1. On the SageMaker console, choose Domains in the navigation pane.
2. Choose the domain you created.
   
3. Choose Assign users and groups.
   
4. On the Users tab, select the user you created.
5. Choose Assign users and groups.
   

Access the SageMaker Canvas application from IAM Identity Center

The user will receive an email with a link to set up a password and instructions to connect to the AWS access portal. The link will be valid for up to 7 days.

When the user receives the email, they must complete the following steps to gain access to SageMaker Canvas:

1. Choose Accept invitation from the email.

2. Set a new password to access SageMaker Canvas in the specified account and domain.
   

After authentication has been performed, the user has three options to log in to SageMaker Canvas:

• Option 1 – Access from SageMaker Studio through the IAM Identity Center portal
• Option 2 – Access from SageMaker Canvas through the IAM Identity Center portal, bypassing SageMaker Studio
• Option 3 – Use the IAM Identity Center portal link in IAM Identity Center to access SageMaker Canvas

We go through each of these options in this section.

Option 1

In the first option, the user first accesses SageMaker Studio to access SageMaker Canvas. This option is appropriate for users that should be able to access all relevant applications from SageMaker Studio, including SageMaker Canvas.

1. Navigate to the AWS access portal URL from your email.

2. Log in with the credentials you set for the user.

You will see the application name you configured earlier.

3. Choose the SageMaker Canvas application.
   

You’re redirected to SageMaker Studio.

4. Choose Run Canvas.
   
5. Choose Open Canvas.
   

You’re redirected to SageMaker Canvas.

Option 2

In this option, the user still goes through the IAM Identity Center portal, but bypasses SageMaker Studio to go directly into SageMaker Canvas. This option should be used when access SageMaker Studio is not needed, since the user’s SageMaker login will always take them directly to SageMaker Canvas.

1. On the SageMaker console, choose Domains in the navigation pane.
2. Note down the SageMaker domain ID.
   
3. Open AWS CloudShell or any other CLI and run the following command, providing your domain ID. This command updates the default landing application for the SageMaker domain from SageMaker Studio to SageMaker Canvas:

   aws sagemaker update-domain --domain-id <SAGEMAKER DOMAIN ID> --default-user-settings '{"DefaultLandingUri":"app:Canvas:models","StudioWebPortal":"DISABLED"}'

You will see the following response if the command runs successfully.

4. Navigate to the AWS access portal URL from your email.
5. Log in with the credentials you set for the user.
6. Choose the SageMaker Canvas application.

This time you’re redirected to SageMaker Canvas, bypassing SageMaker Studio.

Option 3

If the default landing application for the SageMaker domain has been updated from SageMaker Studio to SageMaker Canvas in Option 2, a user can also use the IAM Identity Center portal link to access SageMaker Canvas. To do so, choose the AWS access portal URL shown in the identity source on the IAM Identity Center console. You can use this URL as a browser bookmark, or integrated with your custom application for direct SageMaker Canvas access.

Clean up

To avoid incurring future session charges, log out of SageMaker Canvas.

Conclusion

In this post, we discussed how users can securely access SageMaker Canvas using SSO. To do this, we configured IAM Identity Center and linked it to the SageMaker domain where SageMaker Canvas is used. Users are now one click away from using SageMaker Canvas and solving new challenges with no-code ML. This approach supports the secure environment requirements of cloud engineering and security teams, while allowing for the agility and independence of development teams.

To learn more about SageMaker Canvas, check out Announcing Amazon SageMaker Canvas – a Visual, No Code Machine Learning Capability for Business Analysts. SageMaker Canvas also enables collaboration with data science teams. To learn more, see Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and Amazon SageMaker Canvas. For IT administrators, we suggest checking out Setting up and managing Amazon SageMaker Canvas (for IT administrators).

About the Authors

Dhiraj Thakur is a Solutions Architect with Amazon Web Services. He works with AWS customers and partners to provide guidance on enterprise cloud adoption, migration, and strategy. He is passionate about technology and enjoys building and experimenting in the analytics and AI/ML space.

Dan Sinnreich is a Senior Product Manager at AWS, helping democratize ML with low-code/no-code innovations. Previous to AWS, Dan built and commercialized SaaS platforms and time series risk models used by institutional investors to manage risk and optimize investment portfolios. Outside of work, he can be found playing hockey, scuba diving, and reading science fiction.

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