Monthly Archives: November 2023

An established financial services firm with over 140 years in business, Principal is a global investment management leader and serves more than 62 million customers around the world. Principal is conducting enterprise-scale near-real-time analytics to deliver a seamless and hyper-personalized omnichannel customer experience on their mission to make financial security accessible for all. They are processing data across channels, including recorded contact center interactions, emails, chat and other digital channels.

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

Solution requirements

Principal provides investment services through Genesys Cloud CX, a cloud-based contact center that provides powerful, native integrations with AWS. Each year, Principal handles millions of calls and digital interactions. As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics.

In order analyze the calls properly, Principal had a few requirements:

• Contact details: Understanding the customer journey requires understanding whether a speaker is an automated interactive voice response (IVR) system or a human agent and when a call transfer occurs between the two.
• Content redaction: Each customer audio interaction is recorded as a stereo WAV file, but could potentially include sensitive information such as HIPAA-protected and personally identifiable information (PII).
• Scalability: This architecture needed to immediately scale to thousands of calls per day and millions of calls per year. In addition, Principal needed an extensible analytics architecture that analyze other channels such as email threads and traditional voice of the customer (VoC) survey results.
• Integrity is non-negotiable at Principal—it guides everything they do. In fact, doing what’s right is one of the core values at Principal. Therefore, when the Principal team started tackling this project, they knew that ensuring the highest standard of data security such as regulatory compliance, data privacy, and data quality would be a non-negotiable, key requirement. The team needed to utilize technology with a matching stance on data security, and the ability to build custom compliance and security controls to uphold strict requirements. Attention to this key requirement allows Principal to maintain a safe and secure customer experience.

Solution overview

After extensive research, the Principal team finalized AWS Contact Center Intelligence (CCI) solutions, which empower companies to improve customer experience and gain conversation insights by adding AI capabilities to third-party on-premises and cloud contact centers. The CCI Post-Call Analytics (PCA) solution is part of CCI solutions suite and fit many of the identified requirements. PCA has a Solutions Library Guidance reference architecture with an open-source example repository on GitHub. Working with their AWS account team, Principal detailed the PCA solution and its deployment, and set up custom training programs and immersion days to rapidly upskill the Principal teams. The example architecture (see the following diagram) and code base in the open-source repository allowed the Principal engineering teams to jumpstart their solution around unifying the customer journey, and merging telephony records and transcript records together.

PCA provides an entire architecture around ingesting audio files in a fully automated workflow with AWS Step Functions, which is initiated when an audio file is delivered to a configured Amazon Simple Storage Service (Amazon S3) bucket. After a few minutes, a transcript is produced with Amazon Transcribe Call Analytics and saved to another S3 bucket for processing by other business intelligence (BI) tools. PCA also offers a web-based user interface that allows customers to browse call transcripts. PCA’s security features ensure that any PII data was redacted from the transcript, as well as from the audio file itself. Additionally, all data within the S3 bucket can be encrypted with keys belonging to Principal.

Principal worked with AWS technical teams to modify the Step Functions workflow within PCA to further achieve their goals. Call details such as interaction timestamps, call queues, agent transfers, and participant speaking times are tracked by Genesys in a file called a Contact Trace Record (CTR). Combining accurate transcripts with Genesys CTR files, Principal could properly identify the speakers, categorize the calls into groups, analyze agent performance, identify upsell opportunities, and conduct additional machine learning (ML)-powered analytics.

The teams built a new data ingestion mechanism, allowing the CTR files to be jointly delivered with the audio file to an S3 bucket. Principal and AWS collaborated on a new AWS Lambda function that was added to the Step Functions workflow. This Lambda function identifies CTR records and provides an additional processing step that outputs an enhanced transcript containing additional metadata such as queue and agent ID information, IVR identification and tagging, and how many agents (and IVRs) the customer was transferred to, all aggregated from the CTR records. This extra information enables Principal to create a map of the customer interaction throughout the lifecycle of the conversation and focus on the critical speech segments, while excluding less relevant ones.

Additionally, this postprocessing step enabled Principal to further enrich transcripts with internal information such as agent and queue names and expand the analytics capabilities of PCA, including custom NLP-based ML models for topic and customer intent identification, deployed using Amazon SageMaker endpoints, and additional transcript augmentation using foundational generative AI models hosted on Amazon Bedrock.

PCA is open source on GitHub, which allows customers such as Principal to extend and maintain their own forks with customized, private business code. It also allows the community to submit code back to the main repository for others to use. Principal and AWS technical teams partnered to merge the Genesys CTR and postprocessing placeholder features into the main release of PCA. This partnership between Principal and AWS enabled speed-to-market for Principal, while ensuring that existing and incoming business requirements could be rapidly added. The contributions to the open-source project has accelerated other customers’ Genesys CTR workloads.

Answer business questions

Once PCA was in place, Principal analysts, data scientists, engineers, and business owners worked with AWS SMEs to build numerous Amazon QuickSight dashboards to display the data insights and begin answering business questions. QuickSight is a cloud-scale BI service that you can use to deliver easy-to-understand insights from multiple datasets, from AWS data, third-party data, software as a service (SaaS) data, and more. The use of this BI tool, with its native integrations to the existing data repositories made accessible by Amazon Athena, made the creation of visualizations to display the large-scale data relatively straightforward, and enabled self-service BI. Visualizations were quickly drafted to answer some key questions, including “What are our customers calling us about,” “What topics relate to the longest AHT/most transfers,” and “What topics and issues relate to the lowest customer sentiment scores?” By ingesting additional data related to Principal custom topic models, the team was able to expand their use of QuickSight to include topic and correlation comparisons, model validation capabilities, and comparisons of sentiment based on speaker, segment, call, and conversation. In addition, the use of QuickSight insights quickly allowed the Principal teams to implement anomaly detection and volume prediction, while Amazon QuickSight Q, an ML feature within QuickSight that uses NLP, enabled rapid natural language quantitative data analytics.

When the initial initiative for PCA was complete, Principal knew they needed to immediately dive deeper into the omnichannel customer experience. Together, Principal and AWS have built data ingestion pipelines for customer email interactions and additional metadata from their customer data platform, and built data aggregation and analytics mechanisms to combine omnichannel data into a single customer insight lens. Utilization of Athena views and QuickSight dashboards has continued to enable classic analytics, and the implementation of proof of concept graph databases via Amazon Neptune will help Principal extract insights into interaction topics and intent relationships within the omnichannel view when implemented at scale.

The Results

PCA helped accelerate time to market. Principal was able to deploy the existing open-source PCA app by themselves in 1 day. Then, Principal worked together with AWS and expanded the PCA offering with numerous features like the Genesys CTR integration over a period of 3 months. The development and deployment process was a joint, iterative process that allowed Principal to test and process production call volumes on newly built features. Since the initial engagement, AWS and Principal continue to work together, sharing business requirements, roadmaps, code, and bug fixes to expand PCA.

Since its initial development and deployment, Principal has processed over 1 million customer calls through the PCA framework. This resulted in over 63 million individual speech segments spoken by a customer, agent, or IVR. With this wealth of data, Principal has been able to conduct large-scale historical and near-real-time analytics to gain insights into the customer experience.

AWS CCI solutions are a game-changer for Principal. Principal’s existing suite of CCI tools, which includes Qualtrics for simple dashboarding and opportunity identification, was expanded with the addition of PCA. The addition of PCA to the suite of CCI tools enabled Principal to rapidly conduct deep analytics on their contact center interactions. With this data, Principal now can conduct advanced analytics to understand customer interactions and call drivers, including topics, intents, issues, action items, and outcomes. Even in a small-scale, controlled production environment, the PCA data lake has spawned numerous new use cases.

Roadmap

The data generated from PCA could be used to make critical business decisions regarding call routing based on insights around which topics are driving longer average handle time, longer holds, more transfers, and negative customer sentiment. Knowledge on when customer interactions with the IVR and automated voice assistants are misunderstood or misrouted will help Principal improve the self-service experience. Understanding why a customer called instead of using the website is critical to improving the customer journey and boosting customer happiness. Product managers responsible for enhancing web experiences have shared how excited they are to be able to use data from PCA to drive their prioritization of new enhancements and measure the impact of changes. Principal is also analyzing other potential use cases such as customer profile mapping, fraud detection, workforce management, the use of additional AI/ML and large language models (LLMs), and identifying new and emerging trends within their contact centers.

In the future, Principal plans to continue expanding postprocessing capabilities with additional data aggregation, analytics, and natural language generation (NLG) models for text summarization. Principal is currently integrating generative AI and foundational models (such as Amazon Titan) to their proprietary solutions. Principal plans to use AWS generative AI to enhance employee productivity, grow assets under management, deliver high-quality customer experiences, and deliver tools that allow customers to make investment and retirement decisions efficiently. Given the flexibility and extensibility of the open-source PCA framework, the teams at Principal have an extensive list of additional enhancements, analytics, and insights that could extend the existing framework.

“With AWS Post Call analytics solution, Principal can currently conduct large-scale historical analytics to understand where customer experiences can be improved, generate actionable insights, and prioritize where to act. Now, we are adding generative AI using Amazon Bedrock to help our business users make data-driven decisions with higher speed and accuracy, while reducing costs. We look forward to exploring the post call summarization feature in Amazon Transcribe Call Analytics in order to enable our agents to focus their time and resources engaging with customers, rather than manual after contact work.”

– says Miguel Sanchez Urresty, Director of Data & Analytics at Principal Financial Group.

Conclusion

The AWS CCI PCA solution is designed to improve customer experience, derive customer insights, and reduce operational costs by adding AI and ML to the contact center provider of your choice. To learn more about other CCI solutions, such as Live Call Analytics, refer to AWS Contact Center Intelligence (CCI) Solutions.

About Principal Financial Group

Principal Financial Group and affiliates, Des Moines IA is a financial company with 19,000 employees. In business for more than 140 years, we’re helping more than 62 million customers in various countries around the world as of December 31, 2022.

AWS and Amazon are not affiliates of any company of the Principal Financial Group Insurance products issued by Principal National Life Insurance Co (except in NY) and Principal Life Insurance Company. Plan administrative services offered by Principal Life. Principal Funds, Inc. is distributed by Principal Funds Distributor, Inc. Securities offered through Principal Securities, Inc., member SIPC and/or independent broker/dealers. Referenced companies are members of the Principal Financial Group, Des Moines, IA 50392. ©2023 Principal Financial Services, Inc.

This communication is intended to be educational in nature and is not intended to be taken as a recommendation. Insurance products and plan administrative services provided through Principal Life Insurance Company, a member of the Principal Financial Group, Des Moines, IA 50392

About the authors

Christopher Lott is a Senior Solutions Architect in the AWS AI Language Services team. He has 20 years of enterprise software development experience. Chris lives in Sacramento, California, and enjoys gardening, cooking, aerospace/general aviation, and traveling the world.

Dr. Nicki Susman is a Senior Data Scientist and the Technical Lead of the Principal Language AI Services team. She has extensive experience in data and analytics, application development, infrastructure engineering, and DevSecOps.

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Prompt engineering has become an essential skill for anyone working with large language models (LLMs) to generate high-quality and relevant texts. Although text prompt engineering has been widely discussed, visual prompt engineering is an emerging field that requires attention. Visual prompts can include bounding boxes or masks that guide vision models in generating relevant and accurate outputs. In this post, we explore the basics of visual prompt engineering, its benefits, and how it can be used to solve a specific use case: image segmentation for autonomous driving.

In recent years, the field of computer vision has witnessed significant advancements in the area of image segmentation. One such breakthrough is the Segment Anything Model (SAM) by Meta AI, which has the potential to revolutionize object-level segmentation with zero-shot or few-shot training. In this post, we use the SAM model as an example foundation vision model and explore its application to the BDD100K dataset, a diverse autonomous driving dataset for heterogeneous multitask learning. By combining the strengths of SAM with the rich data provided by BDD100K, we showcase the potential of visual prompt engineering with different versions of SAM. Inspired by the LangChain framework for language models, we propose a visual chain to perform visual prompting by combining object detection models with SAM.

Although this post focuses on autonomous driving, the concepts discussed are applicable broadly to domains that have rich vision-based applications such as healthcare and life sciences, and media and entertainment. Let’s begin by learning a little more about what’s under the hood of a foundational vision model like SAM. We used Amazon SageMaker Studio on an ml.g5.16xlarge instance for this post.

Segment Anything Model (SAM)

Foundation models are large machine learning (ML) models trained on vast quantity of data and can be prompted or fine-tuned for task-specific use cases. Here, we explore the Segment Anything Model (SAM), which is a foundational model for vision, specifically image segmentation. It is pre-trained on a massive dataset of 11 million images and 1.1 billion masks, making it the largest segmentation dataset as of writing. This extensive dataset covers a wide range of objects and categories, providing SAM with a diverse and large-scale training data source.

The SAM model is trained to understand objects and can output segmentation masks for any object in images or video frames. The model allows for visual prompt engineering, enabling you to provide inputs such as text, points, bounding boxes, or masks to generate labels without altering the original image. SAM is available in three sizes: base (ViT-B, 91 million parameters), large (ViT-L, 308 million parameters), and huge (ViT-H, 636 million parameters), catering to different computational requirements and use cases.

The primary motivation behind SAM is to improve object-level segmentation with minimal training samples and epochs for any objects of interest. The power of SAM lies in its ability to adapt to new image distributions and tasks without prior knowledge, a feature known as zero-shot transfer. This adaptability is achieved through its training on the expansive SA-1B dataset, which has demonstrated impressive zero-shot performance, surpassing many prior fully supervised results.

As shown in the following architecture for SAM, the process of generating segmentation masks involves three steps:

1. An image encoder produces a one-time embedding for the image.
2. A prompt encoder converts any prompt into an embedding vector for the prompt.
3. The lightweight decoder combines the information from the image encoder and the prompt encoder to predict segmentation masks.

As an example, we can provide an input with an image and bounding box around an object of interest in that image (e.g. Silver car or driving lane)  and SAM model would produce segmentation masks for that object.

Visual prompt engineering

Prompt engineering refers to structuring inputs to a model that makes the model understand the intent and produces desired outcome. With textual prompt engineering, you can structure the input text through modifications such as choice of words, formatting, ordering, and more to get the desired output. Visual prompt engineering assumes that the user is working in a visual modality (image or video), and provides inputs. The following is a non-exhaustive list of potential ways to provide input to the generative AI model in the visual domain:

• Point – A singular (x, y) coordinate point in the image plane
• Points – Multiple (x, y) coordinate points, not necessarily related to each other
• Bounding box – A set of four values (x, y, w, h) that define a rectangular region in the image plane
• Contour – A set of (x, y) coordinate points in the image plane that form a closed shape
• Mask – An array the same size as the image with a partial mask of the object of interest

With the visual prompt engineering techniques in mind, let’s explore how this can be applied to the SAM pre-trained model. We have use the base version of the pre-trained model.

Zero-shot prompting with the pre-trained SAM model

To start with, let’s explore the zero-shot approach. The following is a sample image from the training dataset taken from a vehicle’s front camera.

We can get segmentation masks for all objects from the image without any explicit visual prompting by automatically generating masks with just an input image. In the following image, we see parts of the car, road, traffic sign, license plates, flyover, pillars, signs, and more are segmented.

However, this output is not immediately useful for the following reasons:

• The cars are not segmented as a whole, but in parts. For most perception models, for example, we don’t really care about each of the tires having separate output masks. This is true when looking for other known objects of interest as well, such as road, vegetation, signs, and so on.
• Parts of the image that are useful for downstream tasks like drivable area are split up, with no explanation. On the other hand, similar instances are identified separately, and we may be interested in grouping similar objects (panoptic vs. instance segmentation).

Visual prompt engineering with the pre-trained SAM model

Fortunately, SAM supports providing input prompts, and we can use points, point arrays, and bounding boxes as inputs. With these specific instructions, we expect SAM to do better with segmentations focusing on specific points or areas. This can be compared with the language prompt template
"What is a good name for a company that makes {product}?"
where the input along with this prompt template from the user is the {product}. {product} is an input slot. In visual prompting, the bounding boxes, points, or masks are the input slots.

The following image provides the original ground truth bounding box around vehicles, and the drivable area patch from BDD100K ground truth data. The image also shows an input point (a yellow X) at the center of the green bounding box that we will refer to in the next few sections.

Let’s try to generate a mask for the car on the left with the green bounding box as an input to SAM. As shown in the following example, the base model of SAM doesn’t really find anything. This is also seen in the low segmentation score. When we look at the segmentation masks more closely, we see that there are small regions returned as masks (pointed at using red arrows) that aren’t really usable for any downstream application.

Let’s try a combination of a bounding box and a point as the input visual prompt. The yellow cross in the preceding image is the center of the bounding box. Providing this point’s (x,y) coordinates as the prompt along with the bounding box constraint gives us the following mask and a slightly higher score. This is still not usable by any means.

Finally, with the base pre-trained model, we can provide just the input point as a prompt (without the bounding box). The following images show two of the top three masks we thought were interesting.

Mask 1 segments the full car, whereas Mask 3 segments out an area that holds the car’s number plate close to the yellow cross (input prompt). Mask 1 is still not a tight, clean mask around the car; this points to the quality of the model, which we can assume increases with model size.

We can try larger pre-trained models with the same input prompt. The following images show our results. When using the huge SAM pre-trained model, Mask 3 is the entire car, whereas Mask 1 and 2 can be used to extract the number plate.

The large version of the SAM model also provides similar outputs.

The process we went through here is similar to manual prompt engineering for text prompts that you may already be familiar with. Note that a recent improvement in the SAM model to segment anything in high quality provides much better object- and context-specific outputs. In our case, we find that zero-shot prompting with text and visual prompts (point, box, and point and box inputs) don’t improve results drastically as we saw above.

Prompt templates and visual chains

As we can see from the preceding zero-shot examples, SAM struggles to identify all the objects in the scene. This is a good example of where we can take advantage of prompt templates and visual chains. Visual chain is inspired by the chain concept in the popular LangChain framework for language applications. It helps chain the data sources and an LLM to produce the output. For example, we can use an API chain to call an API and invoke an LLM to answer the question based on the API response.

Inspired by LangChain, we propose a sequential visual chain that looks like the following figure. We use a tool (like a pre-trained object detection model) to get initial bounding boxes, calculate the point at the center of the bounding box, and use this to prompt the SAM model with the input image.

For example, the following image shows the segmentation masks as a result of running this chain.

Another example chain can involve a text input of the object the user is interested in identifying. To implement this, we built a pipeline using Grounding DINO, an object detection model to prompt SAM for segmentation.

Grounding DINO is a zero-shot object detection model that can perform object detection with text providing category names (such as “traffic lights” or “truck”) and expressions (such as “yellow truck”). It accepts pairs of text and image to perform the object detection. It’s based on a transformer architecture and enables cross modalities with text and image data. To learn more about Grounding DINO, refer to Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection. This generates bounding boxes and labels and can be processed further to generate center points, filter based on labels, thresholds, and more. This is used (boxes or points) as a prompt to SAM for segmentation, which outputs masks.

The following are some examples showing the input text, DINO output (bounding boxes), and the final SAM output (segmentation masks).

The following images show the output for “yellow truck.”

The following images show the output for “silver car.”

The following image shows the output for “driving lane.”

We can use this pipeline to build a visual chain. The following code snippet explains this concept:

pipeline = [object_predictor, segment_predictor]
image_chain = ImageChain.from_visual_pipeline(pipeline, image_store, verbose=True)
image_chain.run('All silver cars', image_id='5X3349')

Although this is a simple example, this concept can be extended to process feeds from cameras on vehicles to perform object tracking, personally identifiable information (PII) data redaction, and more. We can also get the bounding boxes from smaller models, or in some cases, using standard computer vision tools. It’s fairly straightforward to use a pre-trained model or a service like Amazon Rekognition to get initial (visual) labels for your prompt. At the time of writing this, there are over 70 models available on Amazon SageMaker Jumpstart for object detection, and Amazon Rekognition already identifies several useful categories of objects in images, including cars, pedestrians, and other vehicles.

Next, we look at some quantitative results related to performance of SAM models with a subset of BDD100K data.

Quantitative results

Our objective is to compare the performance of three pre-trained models when given the same visual prompting. In this case, we use the center point of the object location as the visual input. We compare the performance with respect to the object sizes (in proportion to image size)— small (area <0.11%), medium (0.11% < area < 1%), and large (area > 1%). The bounding box area thresholds are defined by the Common Objects in Context (COCO) evaluation metrics [Lin et al., 2014].

The evaluation is at the pixel level and we use the following evaluation metrics:

• Precision = (number relevant and retrieved instances) / (total number of retrieved instances)
• Recall = (number of relevant and retrieve instances) / (total number of relevant instances)
• Instances here are each pixel within the bounding box of the object of interest

The following table reports the performance of three different versions of the SAM model (base, large, and huge). These versions have three different encoders: ViT-B (base), ViT-L (large), ViT-H (huge). The encoders have different parameter counts, where the base model has less parameters than large, and large is less than huge. Although increasing the number of parameters shows improved performance with larger objects, this is not so for smaller objects.

Fine-tuning SAM for your use case

In many cases, directly using a pre-trained SAM model may not be very useful. For example, let’s look at a typical scene in traffic—the following picture is the output from the SAM model with randomly sampled prompt points as input on the left, and the actual labels from the semantic segmentation task from BDD100K on the right. These are obviously very different.

Perception stacks in AVs can easily use the second image, but not the first. On the other hand, there some useful outputs from the first image that can be used, and that the model was not explicitly trained on, for example, lane markings, sidewalk segmentation, license plate masks, and so on. We can fine-tune the SAM model to improve the segmentation results. To perform this fine-tuning, we created a training dataset using an instance segmentation subset (500 images) from the BDD10K dataset. This is a very small subset of images, but our purpose is to prove that foundational vision models (much like LLMs) can perform well for your use case with a surprisingly small number of images. The following image shows the input image, output mask (in blue, with a red border for the car on the left), and possible prompts (bounding box in green and center point X in yellow).

We performed fine-tuning using the Hugging Face library on Amazon SageMaker Studio. We used the ml.g4dn.xlarge instance for the SAM base model tests, and the ml.g4dn.2xlarge for the SAM huge model tests. In our initial experiments, we observed that fine-tuning the base model with just bounding boxes was not successful. The fine-tuned and pre-trained models weren’t able to learn car-specific ground truth masks from the original datasets. Adding query points to the fine-tuning also didn’t improve the training.

Next, we can try fine-tuning the SAM huge model for 30 epochs, with a very small dataset (500 images). The original ground truth mask looks like the following image for the label type car.

As shown in the following images, the original pre-trained version of the huge model with a specific bounding box prompt (in green) gives no output, whereas the fine-tuned version gives an output (still not accurate but fine-tuning was cut off after 40 epochs, and with a very small training dataset of 500 images). The original, pre-trained huge model wasn’t able to predict masks for any of the images we tested. As an example downstream application, the fine-tuned model can be used in pre-labeling workflows such as the one described in Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS.

Conclusion

In this post, we discussed the foundational vision model known as the Segment Anything Model (SAM) and its architecture. We used the SAM model to discuss visual prompting and the various inputs to visual prompting engineering. We explored how different visual prompts perform and their limitations. We also described how visual chains increase performance over using just one prompt, similar to the LangChain API. Next, we provided a quantitative evaluation of three pre-trained models. Lastly, we discussed the fine-tuned SAM model and its results compared to the original base model. Fine-tuning of foundation models helps improve model performance for specific tasks like segmentation. It should be noted that SAM model due to its resource requirements, limits usage for real-time use-cases and inferencing at the edge in its current state. We hope with future iterations and improved techniques, would reduce compute requirements and improve latency.

It is our hope that this post encourages you to explore visual prompting for your use cases. Because this is still an emerging form of prompt engineering, there is much to discover in terms of visual prompts, visual chains, and performance of these tools. Amazon SageMaker is a fully managed ML platform that enables builders to explore large language and visual models and build generative AI applications. Start building the future with AWS today.

About the authors

Gopi Krishnamurthy is a Senior AI/ML Solutions Architect at Amazon Web Services based in New York City. He works with large Automotive customers as their trusted advisor to transform their Machine Learning workloads and migrate to the cloud. His core interests include deep learning and serverless technologies. Outside of work, he likes to spend time with his family and explore a wide range of music.

Shreyas Subramanian is a Principal AI/ML specialist Solutions Architect, and helps customers by using Machine Learning to solve their business challenges using the AWS platform. Shreyas has a background in large scale optimization and Machine Learning, and in use of Machine Learning and Reinforcement Learning for accelerating optimization tasks.

 Sujitha Martin is an Applied Scientist in the Generative AI Innovation Center (GAIIC). Her expertise is in building machine learning solutions involving computer vision and natural language processing for various industry verticals. In particular, she has extensive experience working on human-centered situational awareness and knowledge infused learning for highly autonomous systems.

Francisco Calderon is a Data Scientist in the Generative AI Innovation Center (GAIIC). As a member of the GAIIC, he helps discover the art of the possible with AWS customers using Generative AI technologies. In his spare time, Francisco likes to play music and guitar, playing soccer with his daughters, and enjoying time with his family.

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