All posts by : Shelbee Eigenbrode

Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale.

You can extend your pipelines to include steps for tasks performed outside of Amazon SageMaker by taking advantage of custom callback steps. This feature lets you include tasks that are performed using other AWS services, third parties, or tasks run outside AWS. Before the launch of this feature, steps within a pipeline were limited to the supported native SageMaker steps. With the launch of this new feature, you can use the new CallbackStep to generate a token and add a message to an Amazon Simple Queue Service (Amazon SQS) queue. The message on the SQS queue triggers a task outside of the currently supported native steps. When that task is complete, you can call the new SendStepSuccess API with the generated token to signal that the callback step and corresponding tasks are finished and the pipeline run can continue.

In this post, we demonstrate how to use CallbackStep to perform data preprocessing using AWS Glue. We use an Apache Spark job to prepare NYC taxi data for ML training. The raw data has one row per taxi trip, and shows information like the trip duration, number of passengers, and trip cost. To train an anomaly detection model, we want to transform the raw data into a count of the number of passengers that took taxi rides over 30-minute intervals.

Although we could run this specific Spark job in SageMaker Processing, we use AWS Glue for this post. In some cases, we may need capabilities that Amazon EMR or AWS Glue offer, like support for Hive queries or integration with the AWS Glue metadata catalog, so we demonstrate how to invoke AWS Glue from the pipeline.

Solution overview

The pipeline step that launches the AWS Glue job sends a message to an SQS queue. The message contains the callback token we need to send success or failure information back to the pipeline. This callback token triggers the next step in the pipeline. When handling this message, we need a handler that can launch the AWS Glue job and reliably check for job status until the job completes. We have to keep in mind that a Spark job can easily take longer than 15 minutes (the maximum duration of a single AWS Lambda function invocation), and the Spark job itself could fail for a number of reasons. That last point is worth emphasizing: in most Apache Spark runtimes, the job code itself runs in transient containers under the control of a coordinator like Apache YARN. We can’t add custom code to YARN, so we need something outside the job to check for completion.

We can accomplish this task several ways:

• Have a Lambda function launch a container task that creates the AWS Glue job and polls for job completion, then sends the callback back to the pipeline
• Have a Lambda function send a work notification to another SQS queue, with a separate Lambda function that picks up the message, checks for job status, and requeues the message if the job isn’t complete
• Use AWS Glue job event notifications to respond to job status events sent by AWS Glue

For this post, we use the first technique because it’s the simplest (but likely not the most efficient). For this, we build out the solution as shown in the following diagram.

The solution is one example of how to use the new CallbackStep to extend your pipeline to steps outside SageMaker (such as AWS Glue). You can apply the same general steps and architectural guidance to extend pipelines to other custom processes or tasks. In our solution, the pipeline runs the following tasks:

Data preprocessing –

• This step (Step 1 in the preceding diagram) uses CallbackStep to send a generated token and defined input payload to the configured SQS queue (2). In this example, the input sent to the SQS queue is the Amazon Simple Storage Service (Amazon S3) locations of the input data and the step output training data.
  • The new message in the SQS queue triggers a Lambda function (3) that is responsible for running an AWS Fargate task with Amazon Elastic Container Service (Amazon ECS) (4).
  • The Fargate task runs using a container image that is configured to run a task. The task in this case is an AWS Glue job (5) used to transform your raw data into training data stored in Amazon S3 (6). This task is also responsible for sending a callback message that signals either the job’s success or failure.
• Model training – This step (7) runs when the previous callback step has completed successfully. It uses the generated training data to train a model using a SageMaker training job and the Random Cut Forest algorithm.
• Package model – After the model is successfully trained, the model is packaged for deployment (8).
• Deploy model – In this final step (9), the model is deployed using a batch transform job.

These pipeline steps are just examples; you can modify the pipeline to meet your use case, such as adding steps to register the model in the SageMaker Model Registry.

In the next sections, we discuss how to set up this solution.

Prerequisites

For the preceding pipeline, you need the prerequisites outlined in this section. The detailed setup of each of these prerequisites is available in the supporting notebook.

Notebook dependencies

To run the provided notebook, you need the following:

• Studio environment – Callback steps were introduced in the SageMaker Python SDK v2.45.0 and Amazon SageMaker Studio v3.6.2. Update Studio if needed before you run the sample notebook.
• Studio Image Build CLI – Because we build a container image for the Fargate task from within the notebook, a step is included to install the Studio Image Build CLI. For more information about the CLI dependencies, see Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks.

Pipeline dependencies

Your pipeline uses the following services:

• SQS message queue – The callback step requires an SQS queue to trigger a task. For this, you need to create an SQS queue and ensure that AWS Identity and Access Management (IAM) permissions are in place that allow SageMaker to put a message in the queue and allow Lambda to poll the queue for new messages. See the following code:

sqs_client = boto3.client('sqs')
queue_url = ''
queue_name = 'pipeline_callbacks_glue_prep'
try:
    response = sqs_client.create_queue(QueueName=queue_name)
except:
    print(f"Failed to create queue")

• Lambda function: The function is triggered by new messages put to the SQS queue. The function consumes these new messages and starts the ECS Fargate task. In this case, the Lambda execution IAM role needs permissions to pull messages from Amazon SQS, notify SageMaker of potential failures, and run the Amazon ECS task. For this solution, the function starts a task on ECS Fargate using the following code:

%%writefile queue_handler.py
import json
import boto3
import os
import traceback

ecs = boto3.client('ecs')
sagemaker = boto3.client('sagemaker')

def handler(event, context):   
    print(f"Got event: {json.dumps(event)}")
    
    cluster_arn = os.environ["cluster_arn"]
    task_arn = os.environ["task_arn"]
    task_subnets = os.environ["task_subnets"]
    task_sgs = os.environ["task_sgs"]
    glue_job_name = os.environ["glue_job_name"]
    print(f"Cluster ARN: {cluster_arn}")
    print(f"Task ARN: {task_arn}")
    print(f"Task Subnets: {task_subnets}")
    print(f"Task SG: {task_sgs}")
    print(f"Glue job name: {glue_job_name}")
    
    for record in event['Records']:
        payload = json.loads(record["body"])
        print(f"Processing record {payload}")
        
        token = payload["token"]
        print(f"Got token {token}")
        
        try:
            input_data_s3_uri = payload["arguments"]["input_location"]
            output_data_s3_uri = payload["arguments"]["output_location"]
            print(f"Got input_data_s3_uri {input_data_s3_uri}")
            print(f"Got output_data_s3_uri {output_data_s3_uri}")

            response = ecs.run_task(
                cluster = cluster_arn,
                count=1,
                launchType='FARGATE',
                taskDefinition=task_arn,
                networkConfiguration={
                    'awsvpcConfiguration': {
                        'subnets': task_subnets.split(','),
                        'securityGroups': task_sgs.split(','),
                        'assignPublicIp': 'ENABLED'
                    }
                },
                overrides={
                    'containerOverrides': [
                        {
                            'name': 'FargateTask',
                            'environment': [
                                {
                                    'name': 'inputLocation',
                                    'value': input_data_s3_uri
                                },
                                {
                                    'name': 'outputLocation',
                                    'value': output_data_s3_uri
                                },
                                {
                                    'name': 'token',
                                    'value': token
                                },
                                {
                                    'name': 'glue_job_name',
                                    'value': glue_job_name
                                }
                                
                            ]
                        }
                    ]
                }
            )
            if 'failures' in response and len(response['failures']) > 0:
                f = response['failures'][0]
                print(f"Failed to launch task for token {token}: {f['reason']}")
                sagemaker.send_step_failure(
                    CallbackToken=token,
                    FailureReason = f['reason']
                )
            else:
                print(f"Launched task {response['tasks'][0]['taskArn']}")
        except Exception as e:
            trc = traceback.format_exc()
            print(f"Error handling record: {str(e)}:m {trc}")
            sagemaker.send_step_failure(
                CallbackToken=token,
                FailureReason = e
            )

• After we create the SQS queue and Lambda function, we need to set up the function as an SQS target so that when new messages are placed in the queue, the function is automatically triggered:

lambda_client.create_event_source_mapping(
    EventSourceArn=f'arn:aws:sqs:{region}:{account}:{queue_name}',
    FunctionName='SMPipelineQueueHandler',
    Enabled=True,
    BatchSize=10
) 

• Fargate cluster – Because we use Amazon ECS to run and monitor the status of the AWS Glue job, we need to ensure we have an ECS Fargate cluster running:

import boto3
ecs = boto3.client('ecs')
response = ecs.create_cluster(clusterName='FargateTaskRunner')

• Fargate task: We also need to create a container image with the code (task.py) that starts the data preprocessing job on AWS Glue and reports the status back to the pipeline upon the success or failure of that task. The IAM role attached to the task must include permissions that allow the task to pull images from Amazon ECR, create logs in Amazon CloudWatch, start and monitor an AWS Glue job, and send the callback token when the task is complete. When we issue send_pipeline_execution_step_success back to the pipeline, we also indicate the output file with the prepared training data. We use the output parameter in the model training step in the pipeline. The following is the code for task.py:

import boto3
import os
import sys
import traceback
import time

if 'inputLocation' in os.environ:
    input_uri = os.environ['inputLocation']
else:
    print("inputLocation not found in environment")
    sys.exit(1)
if 'outputLocation' in os.environ:
    output_uri = os.environ['outputLocation']
else:
    print("outputLocation not found in environment")
    sys.exit(1)
if 'token' in os.environ:
    token = os.environ['token']
else:
    print("token not found in environment")
    sys.exit(1)
if 'glue_job_name' in os.environ:
    glue_job_name = os.environ['glue_job_name']
else:
    print("glue_job_name not found in environment")
    sys.exit(1)

print(f"Processing from {input_uri} to {output_uri} using callback token {token}")
sagemaker = boto3.client('sagemaker')
glue = boto3.client('glue')

poll_interval = 60

try:
    
    t1 = time.time()
    response = glue.start_job_run(
        JobName=glue_job_name,
        Arguments={
            '--output_uri': output_uri,
            '--input_uri': input_uri
        }
    )
    job_run_id = response['JobRunId']
    print(f"Starting job {job_run_id}")
    
    job_status = 'STARTING'
    job_error = ''
    while job_status in ['STARTING','RUNNING','STOPPING']:
        time.sleep(poll_interval)
        response = glue.get_job_run(
            JobName=glue_job_name,
            RunId=job_run_id,
            PredecessorsIncluded=False
        )
        job_status = response['JobRun']['JobRunState']
        if 'ErrorMessage' in response['JobRun']:
            job_error = response['JobRun']['ErrorMessage']
        print(f"Job is in state {job_status}")
        
    t2 = time.time()
    total_time = (t2 - t1) / 60.0
    if job_status == 'SUCCEEDED':
        print("Job succeeded")
        sagemaker.send_pipeline_execution_step_success(
            CallbackToken=token,
            OutputParameters=[
                {
                    'Name': 'minutes',
                    'Value': str(total_time)
                },
                {
                    'Name': 's3_data_out',
                    'Value': str(output_uri),
                } 
            ]
        )
    else:
        print(f"Job failed: {job_error}")
        sagemaker.send_pipeline_execution_step_failure(
            CallbackToken=token,
            FailureReason = job_error
        )
except Exception as e:
    trc = traceback.format_exc()
    print(f"Error running ETL job: {str(e)}:m {trc}")
    sagemaker.send_pipeline_execution_step_failure(
        CallbackToken=token,
        FailureReason = str(e)
    )

• Data preprocessing code – The pipeline callback step does the actual data preprocessing using a PySpark job running in AWS Glue, so we need to create the code that is used to transform the data:

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from pyspark.sql.types import IntegerType
from pyspark.sql import functions as F

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME', 'input_uri', 'output_uri'])

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

df = spark.read.format("csv").option("header", "true").load("{0}*.csv".format(args['input_uri']))
df = df.withColumn("Passengers", df["passenger_count"].cast(IntegerType()))
df = df.withColumn(
  'pickup_time',
  F.to_timestamp(
  F.unix_timestamp('tpep_pickup_datetime', 'yyyy-MM-dd HH:mm:ss').cast('timestamp')))
  
dfW = df.groupBy(F.window("pickup_time", "30 minutes")).agg(F.sum("Passengers").alias("passenger"))
dfOut = dfW.drop('window')
dfOut.repartition(1).write.option("timestampFormat", "yyyy-MM-dd HH:mm:ss").csv(args['output_uri'])

job.commit()

• Data preprocessing job – We need to also configure the AWS Glue job that runs the preceding code when triggered by your Fargate task. The IAM role used must have permissions to read and write from the S3 bucket. See the following code:

glue = boto3.client('glue')
response = glue.create_job(
    Name='GlueDataPrepForPipeline',
    Description='Prepare data for SageMaker training',
    Role=glue_role_arn,
    ExecutionProperty={
        'MaxConcurrentRuns': 1
    },
    Command={
        'Name': 'glueetl',
        'ScriptLocation': glue_script_location,
    },
    MaxRetries=0,
    Timeout=60,
    MaxCapacity=10.0,
    GlueVersion='2.0'
)
glue_job_name = response['Name']

After these prerequisites are in place, including the necessary IAM permissions outlined in the example notebook, we’re ready to configure and run the pipeline.

Configure the pipeline

To build out the pipeline, we rely on the preceding prerequisites in the callback step that perform data processing. We also combine that with steps native to SageMaker for model training and deployment to create an end-to-end pipeline.

To configure the pipeline, complete the following steps:

1. Initialize the pipeline parameters:

from sagemaker.workflow.parameters import (
    ParameterInteger,
    ParameterString,
)

input_data = ParameterString(
    name="InputData",
    default_value=f"s3://{default_bucket}/{taxi_prefix}/"
)
id_out = ParameterString(
    name="IdOut",
    default_value="taxiout"+ str(timestamp)
)
output_data = ParameterString(
    name="OutputData",
    default_value=f"s3://{default_bucket}/{taxi_prefix}_output/"
)
training_instance_count = ParameterInteger(
    name="TrainingInstanceCount",
    default_value=1
)
training_instance_type = ParameterString(
    name="TrainingInstanceType",
    default_value="ml.c5.xlarge"
)

2. Configure the first step in the pipeline, which is CallbackStep.

This step uses the SQS queue created in the prerequisites in combination with arguments that are used by tasks in this step. These arguments include the inputs of the Amazon S3 location of the input (raw taxi data) and output training data. The step also defines the outputs, which in this case includes the callback output and Amazon S3 location of the training data. The outputs become the inputs to the next step in the pipeline. See the following code:

from sagemaker.workflow.callback_step import CallbackStep,CallbackOutput,CallbackOutputTypeEnum

callback1_output=CallbackOutput(output_name="s3_data_out", output_type=CallbackOutputTypeEnum.String)

step_callback_data = CallbackStep(
                    name="GluePrepCallbackStep",
                    sqs_queue_url=queue_url,
                    inputs={
                        "input_location": f"s3://{default_bucket}/{taxi_prefix}/",
                        "output_location": f"s3://{default_bucket}/{taxi_prefix}_{id_out}/"
                    },
                    outputs=[
                        callback1_output
                    ],
                )

3. We use TrainingStep to train a model using the Random Cut Forest algorithm.

We first need to configure an estimator, then we configure the actual pipeline step. This step takes the output of the previous step and Amazon S3 location of the training data created by AWS Glue as input to train the model. See the following code:

containers = {
    'us-west-2': '174872318107.dkr.ecr.us-west-2.amazonaws.com/randomcutforest:latest',
    'us-east-1': '382416733822.dkr.ecr.us-east-1.amazonaws.com/randomcutforest:latest',
    'us-east-2': '404615174143.dkr.ecr.us-east-2.amazonaws.com/randomcutforest:latest',
    'eu-west-1': '438346466558.dkr.ecr.eu-west-1.amazonaws.com/randomcutforest:latest'}
region_name = boto3.Session().region_name
container = containers[region_name]
model_prefix = 'model'

session = sagemaker.Session()

rcf = sagemaker.estimator.Estimator(
    container,
    sagemaker.get_execution_role(),
    output_path='s3://{}/{}/output'.format(default_bucket, model_prefix),
    instance_count=training_instance_count,
    instance_type=training_instance_type,
    sagemaker_session=session)

rcf.set_hyperparameters(
    num_samples_per_tree=200,
    num_trees=50,
    feature_dim=1)
from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep

step_train = TrainingStep(
    name="TrainModel",
    estimator=rcf,
    inputs={
        "train": TrainingInput(
        #s3_data = Output of the previous call back 
        steps3_data=step_callback_data.properties.Outputs['s3_data_out'],
        content_type="text/csv;label_size=0",
        distribution='ShardedByS3Key'
        ),
    },
)

4. We use CreateModelStep to package the model for SageMaker deployment:

from sagemaker.model import Model
from sagemaker import get_execution_role
role = get_execution_role()

image_uri = sagemaker.image_uris.retrieve("randomcutforest", region)

model = Model(
    image_uri=image_uri,
    model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
    sagemaker_session=sagemaker_session,
    role=role,
    )
from sagemaker.inputs import CreateModelInput
from sagemaker.workflow.steps import CreateModelStep

inputs = CreateModelInput(
    instance_type="ml.m5.large",
)

create_model = CreateModelStep(
    name="TaxiModel",
    model=model,
    inputs=inputs,
)

5. We deploy the trained model using a SageMaker batch transform job using TransformStep.

This step loads the trained model and processes the prediction request data stored in Amazon S3, then outputs the results (anomaly scores in this case) to the specified Amazon S3 location. See the following code:

base_uri = step_callback_data.properties.Outputs['s3_data_out']
output_prefix = 'batch-out'

from sagemaker.transformer import Transformer

transformer = Transformer(
    model_name=create_model.properties.ModelName,
    instance_type="ml.m5.xlarge",
    assemble_with = "Line",
    accept = 'text/csv',
    instance_count=1,
    output_path=f"s3://{default_bucket}/{output_prefix}/",
)
from sagemaker.inputs import TransformInput
from sagemaker.workflow.steps import TransformStep

batch_data=step_callback_data.properties.Outputs['s3_data_out']

step_transform = TransformStep(
    name="TaxiTransform",
    transformer=transformer,
    inputs=TransformInput(data=batch_data,content_type="text/csv",split_type="Line",input_filter="$[0]",join_source='Input',output_filter='$[0,-1]')
)

Create and run the pipeline

You’re now ready to create and run the pipeline. To do this, complete the following steps:

1. Define the pipeline including the parameters accepted and steps:

from sagemaker.workflow.pipeline import Pipeline

pipeline_name = f"GluePipeline-{id_out}"
pipeline = Pipeline(
    name=pipeline_name,
    parameters=[
        input_data,
        training_instance_type,
        training_instance_count,
        id_out,
    ],
    steps=[step_callback_data, step_train,create_model,step_transform],
)

2. Submit the pipeline definition to create the pipeline using the role that is used to create all the jobs defined in each step:

from sagemaker import get_execution_role
pipeline.upsert(role_arn = get_execution_role())

3. Run the pipeline:

execution = pipeline.start()

You can monitor your pipeline using the SageMaker SDK, execution.list_steps(), or via the Studio console, as shown in the following screenshot.

Use CallbackStep to integrate other tasks outside of SageMaker

You can follow the same pattern to integrate any long-running tasks or jobs with Pipelines. This may include running AWS Batch jobs, Amazon EMR job flows, or Amazon ECS or Fargate tasks.

You can also implement an email approval step for your models as part of your ML pipeline.
CallbackStep runs after the model EvaluationStep and sends an email containing approve or reject links with model metrics to a user. The workflow progresses to the next state after the user approves the task to proceed.

You can implement this pattern using a Lambda function and Amazon Simple Notification Service (Amazon SNS).

Conclusion

In this post, we showed you an example of how to use CallbackStep in Pipelines to extend your pipelines to integrate an AWS Glue job for data preprocessing. You can follow the same process to integrate any task or job outside of SageMaker. You can walk through the full solution explained in the example notebook.

About the Author

Shelbee Eigenbrode is a Principal AI and Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). She holds 6 AWS certifications and has been in technology for 23 years spanning multiple industries, technologies, and roles. She is currently focusing on combining her DevOps and ML background to deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes. Shelbee co-founded the Denver chapter of Women in Big Data.

Sofian Hamiti is an AI/ML specialist Solutions Architect at AWS. He helps customers across industries accelerate their AI/ML journey by helping them build and operationalize end-to-end machine learning solutions.

Randy DeFauw is a principal solutions architect at Amazon Web Services. He works with the AWS customers to provide guidance and technical assistance on database projects, helping them improve the value of their solutions when using AWS.

Payton Staub is a senior engineer with Amazon SageMaker. His current focus includes model building pipelines, experiment management, image management and other tools to help customers productionize and automate machine learning at scale.

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Customers expect personalized contact center experiences. They want easy access to customer support and quick resolution of their issues. Delighting callers with a quick and easy interaction remains central to the customer experience (CX) strategy for support organizations. Enterprises often deploy omni-channel contact centers so that they can provide simple mechanisms for their customers to access customer support. But even with these efforts, callers face long wait times, especially during peak hours, which can lead to lower CSAT scores. In addition, organizations have to manage support costs as their footprint expands. As the customer base grows, operational costs for managing a contact center can rapidly increase.

With Amazon Lex bots, you can use conversational AI capabilities to provide highly engaging and lifelike conversational experiences. Organizations can use Amazon Lex to automate customer service interactions and deliver faster responses to queries. As a result, customer issues are resolved in real time, reducing wait times and driving higher satisfaction. You can use Amazon Lex to handle the most common problems encountered by customers. Furthermore, complex issues that require human intervention can be seamlessly handed over from the Amazon Lex bot to a human agent. Augmenting your contact center operations with Amazon Lex bots provides an enhanced caller experience, while optimizing your operational costs with self-service automation. In addition, you can seamlessly scale your contact center operations on the AWS Cloud as your user base grows.

We’re excited to announce Amazon Lex V2 bot support on the Genesys Cloud platform. With this launch, you can build an Amazon Lex bot and set up your contact center in minutes.

About Amazon Lex V2 APIs and Genesys Cloud

Amazon Lex launched V2 APIs and a new console interface that makes it easier to build, deploy, and manage conversational experiences. The Lex V2 console and API enhancements provide support for multiple languages in a single bot, enables simplified versioning, and provides builder productivity tools. These features provide you more control over the bot building and deployment processes.

Genesys Cloud (an omni-channel orchestration and customer relationship platform) provides a contact center platform in a public cloud model that enables quick and simple integration of AWS Contact Center Intelligence (AWS CCI) to transform the modern contact center from a cost center into a profit center. As part of AWS CCI, Genesys cloud integrates with Amazon Lex, Amazon Polly (text to speech) and Amazon Kendra (intelligent search) to offer self-service conversational AI capabilities.

Key features

Genesys Cloud uses the continuous streaming capability with Amazon Lex V2 APIs to enable advanced IVR conversations. With this integration, you can now enable the following:

• Interruptions (“barge-in”) – Callers can now interrupt the bot and answer a question before the prompt is completed
• Wait and Continue – Callers can instruct the bot to wait if they need time for retrieving additional information during the call (such as a credit card number or booking ID)
• DTMF support – Callers can provide information via speech or DTMF interchangeably
• SSML support – You can configure prompts within the Amazon Lex bot using SSML tags, enabling greater control over speech generation from text
• Configurable timeouts – You can configure how long to wait for the customer to finish speaking before Amazon Lex collects speech input from callers, such as answering a yes/no question, or providing a date or credit card number

Creating the bot

Let’s create a banking bot as an example and integrate with Genesys Cloud for IVR-based interactions. For a step-by-step process to build an Amazon Lex bot, refer to banker bot workshop. You can also download the bot and import it using the Amazon Lex V2 console.

In addition to the intents presented in the workshop, we add a SpeakToAgent intent to enable handing over the conversation to a human agent based on user requests.

Enabling the integrations

The Amazon Lex V2 integration is available for installation via Genesys AppFoundry. You need an active subscription for premium applications to access the Integration page from the Genesys Cloud Admin dashboard. Genesys also offers a free trial for validation purposes.

1. Configure the IAM role

As invocations for Amazon Lex take place in your AWS environment, you configure an AWS Identity and Access Management (IAM) role with proper permission for Genesys Cloud to assume the role and use resources.

1. Create an IAM role and select trusted entity to be Another AWS account.
2. Enter the Genesys Cloud production ID 765628985471 in the Account ID field.
3. As part of the AWS best practices, you should select Require external ID and enter your organization’s ID to prevent the confused deputy problem and enhance integration security.

By default, IAM roles don’t have permission to create or modify AWS resources. For Genesys Cloud to successfully access Amazon Lex bots, a few permissions are required.

4. Choose Create Policy and enter the following JSON blob into the policy editor.

{
     "Version": "2012-10-17",
     "Statement": [
          {
               "Sid": "GenesysLexPolicy",
               "Effect": "Allow",
               "Action": [
                    "lex:List*",
                    "lex:Describe*",
                    "lex:StartConversation",
             		"lex:Recognize*",
 					"lex:DeleteSession",
             		"lex:GetSession",
  			  		"lex:PutSession"
              		  ],
                "Resource": "*"
          }
     ]
}

5. Attach the policy to the role created previously.
6. Copy the role ARN and configure it within Genesys Cloud.
7. Save and set the integration status to activate the bot.

2. Configure Amazon Polly

To use Amazon Lex for a voice bot, you set up the text to speech (TTS) capability. Genesys Cloud supports several TTS engines, including Amazon Polly. You can install and configure the Amazon Polly integration following the Genesys documentation. You can then select Amazon Polly as the engine and configure the voice you prefer. To keep the IVR voice consistent in the call flow, the Amazon Polly voice selected in Genesys Cloud should be the same voice configured in your Lex bot. For additional details, see a list of available voices and the associated characteristics.

3. Configure the Genesys Cloud Architect flow

Create an Inbound Call Flow in Architect to orchestrate your bot interaction. You add a Reusable Tasks and use Call Lex V2 bot action to bring in the Amazon Lex bot and design various actions in the call flow.

The integration also allows Genesys Cloud to capture the preconfigured slots as Architect variables. These variables can be used outside of the bot for use-cases such as application of business rules. For example, if a customer provides an account ID that matches with the VIP customer segment, the call can be routed to the priority support queue when transferring to an agent.

4. Configure graceful escalation

When the automated solution can’t fulfill a customer’s request, the interaction should be escalated gracefully. This fallback process allows a human agent to take over the interaction for more complex tasks.

You can save key information from the prior exchange (such as intents, slots, and conversation transcripts) into a script to provide historical context to the agent so that conversations can be picked up seamlessly. This prevents customers from wasting valuable time to repeat the information provided previously.

In the following example, the call is transferred to an available Tier 1 support agent when a customer asks for more help or to be connected to an agent. You can also collect additional context from the customer and hand off to either another bot or human based on specialty.

5. Test the integrations

You can use the native soft phone in Genesys Cloud to make calls as you would with a desktop phone and validate the integration. Enter the bot’s name in the Enter Names and Numbers field and choose Call to follow the prompts.

Summary

Enterprises increasingly invest in automated solutions such as IVR and chatbots as a part of their customer service strategy in contact centers. Automation provides highly available support that handles common tasks without the presence of a live agent, while reducing operational cost.

With the adoption of the Amazon Lex V2 APIs, Genesys Cloud provides an overall improved user experience using the continuous streaming architecture, and enables a more natural customer-bot interaction.

This post outlines the key steps to enable the Amazon Lex V2 integration in your Genesys Cloud environment, and should give you a jump start to create and customize your own chatbot initiative. Check out the following resources for additional information:

• Navigate to the Amazon Lex V2 console and learn more at Amazon Lex V2 Developer Guide
• Get started with Amazon Lex V2 integration on Genesys Cloud AppFoundry
• Learn more about Genesys Lex V2 integration
• Acquire Genesys Cloud from AWS Marketplace

About the Author

Anubhav Mishra is a Product Manager with AWS. He spends his time understanding customers and designing product experiences to address their business challenges.

Jessica Ho is a Solutions Architect at Amazon Web Services supporting ISV partners who build business applications on AWS. She is passionate about creating differentiated solutions that unlock customers for cloud adoption. Outside of work, she enjoys spoiling her garden into a mini jungle.

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Do you want to monitor your business metrics and detect anomalies in your existing streaming data pipelines? Amazon Lookout for Metrics is a service that uses machine learning (ML) to detect anomalies in your time series data. The service goes beyond simple anomaly detection. It allows developers to set up autonomous monitoring for important metrics to detect anomalies and identify their root cause in a matter of few clicks, using the same technology used by Amazon internally to detect anomalies in its metrics—all with no ML experience required. However, one limitation you may face if you have an existing Amazon Kinesis Data Streams data pipeline is not being able to directly run anomaly detection on your data streams using Lookout for Metrics. As of this writing, Lookout for Metrics doesn’t have native integration with Kinesis Data Streams to ingest streaming data and run anomaly detection on it.

In this post, we show you how to solve this problem by using an AWS Glue Spark streaming extract, transform, and load (ETL) script to ingest and organize streaming data in Amazon Simple Storage Service (Amazon S3) and using a Lookout for Metrics live detector to detect anomalies. If you have an existing Kinesis Data Streams pipeline that ingests ecommerce data, for example, you can use the solution to detect anomalies such as unexpected dips in revenue, high rates of abandoned shopping carts, increases in new user signups, and many more.

Included in this post is a sample streaming data generator to help you get started quickly. The included GitHub repo provides step-by-step deployment instructions, and uses the AWS Cloud Development Kit (AWS CDK) to simplify and automate the deployment.

Lookout for Metrics allows users to set up anomaly detectors in both continuous and backtest modes. Backtesting allows you to detect anomalies on historical data. This feature is helpful when you want to try out the service on past data or validate against known anomalies that occurred in the past. For this post, we use continuous mode, where you can detect anomalies on live data as they occur. In continuous mode, the detector monitors an input S3 bucket for continuous data and runs anomaly detection on new data at specified time intervals. For the live detector to consume continuous time series data from Amazon S3 correctly, it needs to know where to look for data for the current time interval, therefore, it requires continuous input data in S3 buckets organized by time interval.

Overview of solution

The solution architecture consists of the following components:

• Streaming data generator – To help you get started quickly, we provide Python code that generates sample time series data and writes to a Kinesis data stream at a specified time interval. The provided code generates sample data for an ecommerce schema (platform, marketplace, event_time, views, revenue). You can also use your own data stream and data, but you must update the downstream processes in the architecture to process your schema.
• Kinesis Data Streams to Lookout for Metrics – The AWS Glue Spark streaming ETL code is the core component of the solution. It contains logic to do the following:
  • Ingest streaming data
  • Micro-batch data by time interval
  • Filter dimensions and metrics columns
  • Deliver filtered data to Amazon S3 grouped by timestamp
• Lookout for Metrics continuous detector – The AWS Glue streaming ETL code writes time series data as CSV files to the S3 bucket, with objects organized by time interval. The Lookout for Metrics continuous detector monitors the S3 bucket for live data and runs anomaly detection at the specified time interval (for example, every 5 minutes). You can view the detected anomalies on the Lookout for Metrics dashboard.

The following diagram illustrates the solution architecture.

AWS Glue Spark streaming ETL script

The main component of the solution is the AWS Glue serverless streaming ETL script. The script contains the logic to ingest the streaming data and write the output, grouped by time interval, to an S3 bucket. This makes it possible for Lookout for Metrics to use streaming data from Kinesis Data Streams to detect anomalies. In this section, we walk through the Spark streaming ETL script used by AWS Glue.

The AWS Glue Spark streaming ETL script performs the following steps:

1. Read from the AWS Glue table that uses Kinesis Data Streams as the data source.

The following screenshot shows the AWS Glue table created for the ecommerce data schema.

2. Ingest the streaming data from the AWS Glue table (table_name parameter) batched by time window (stream_batch_time parameter) and create a data frame for each micro-batch using create_data_frame.from_catalog(), as shown in the following code:

data_frame_datasource0 = glueContext.create_data_frame.from_catalog(stream_batch_time = BATCH_WIN_SIZE, 
                            database = glue_dbname, table_name = glue_tablename, transformation_ctx = "datasource0", 
                            additional_options = {"startingPosition": "TRIM_HORIZON", "inferSchema": "false"})

3. Perform the following processing steps for each batch of data (data frame) ingested:
   1. Select only the required columns and convert the data frame to the AWS Glue native DynamicFrame.

datasource0 = DynamicFrame.fromDF(data_frame, glueContext, "from_data_frame").select_fields(['marketplace','event_time', 'views'])

As shown in the preceding example code, select only the columns marketplace, event_time, and views to write to output CSV files in Amazon S3. Lookout for Metrics uses these columns for running anomaly detection. In this example, marketplace is the optional dimension column used for grouping anomalies, views is the metric to be monitored for anomalies, and event_time is the timestamp for time series data.

1. 2. Populate the time interval in each streaming record ingested:

datasource1 = Map.apply(frame=datasource0, f=populateTimeInterval)

In the preceding code, we provide the custom function populateTimeInterval, which determines the appropriate time interval for the given data point based on its event_time timestamp column.

The following table includes example time intervals determined by the function for a 5-minute frequency.

The following table includes example time intervals determined by the function for a 10-minute frequency.

1. 3. The write_dynamic_frame() function uses the time interval (as determined in the previous step) as the partition key to write output CSV files to the appropriate S3 prefix structure:

datasink1 = glueContext.write_dynamic_frame.from_options(frame = datasource1, connection_type = "s3", 
                        connection_options = {"path": path_datasink1, "partitionKeys": ["intvl_date", "intvl_hhmm"]}, 
                        format_options={"quoteChar": -1, "timestamp.formats": "yyyy-MM-dd HH:mm:ss"}, 
                        format = src_format, transformation_ctx = "datasink1")

For example, the following screenshot shows that the ETL script writes output to the S3 folder structure organized by 5-minute time intervals.

For additional details on partitions for ETL outputs, see Managing Partitions for ETL Output in AWS Glue.

You can set up a live detector using Amazon S3 as a continuous data source to start detecting the anomalies in streaming data. For detailed instructions, see GitHub repo.

Prerequisites

You need the following to deploy the solution:

• An AWS account with permissions to deploy the solution using AWS CDK
• A workstation or development environment with the following installed and configured:
  • npm
  • Typescript
  • AWS CDK
  • AWS account credentials

You can find detailed instructions in the “Getting Started” section of the GitHub repo.

Deploy the solution

Follow the step-by-step instructions in the GitHub repo to deploy the solution components. AWS CDK templates are provided for each of the solution components, organized in their own directory structure within the GitHub repo. The templates deploy the following resources:

• Data generator – The Lambda function, Amazon EventBridge rule, and Kinesis data stream
• Connector for Lookout for Metrics – The AWS Glue Spark streaming ETL job and S3 bucket
• Lookout for Metrics continuous detector – Our continuous detector

Clean up

To avoid incurring future charges, delete the resources by deleting the stacks deployed by the AWS CDK.

Conclusion

In this post, we showed how you can detect anomalies in streaming data sources using a Lookout for Metrics continuous detector. The solution used serverless streaming ETL with AWS Glue to prepare the data for Lookout for Metrics anomaly detection. The reference implementation used an ecommerce sample data schema (platform, marketplace, event_time, views, revenue) to demonstrate and test the solution.

You can easily extend the provided data generator code and ETL script to process your own schema and sample data. Additionally, you can adjust the solution parameters such as anomaly detection frequency to match your use case. With minor changes, you can replace the sample data generator with an existing Kinesis Data Streams streaming data source.

To learn more about Amazon Lookout for Metrics, see Introducing Amazon Lookout for Metrics: An anomaly detection service to proactively monitor the health of your business and the Lookout for Metrics Developer Guide. For additional information about streaming ETL jobs with AWS Glue, see Crafting serverless streaming ETL jobs with AWS Glue and Adding Streaming ETL Jobs in AWS Glue.

About the Author

Babu Srinivasan is a Sr. Solutions Architect at AWS, with over 24 years of experience in IT and the last 6 years focused on the AWS Cloud. He is passionate about AI/ML. Outside of work, he enjoys woodworking and entertains friends and family (sometimes strangers) with sleight of hand card magic.

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