Domain 3: Deployment and Orchestration of ML Workflows (22% of the exam content) - AWS Certification
Task 3.1: Select deployment infrastructure based on existing architecture and requirementsTask 3.2: Create and script infrastructure based on existing architecture and requirementsTask 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines

Domain 3: Deployment and Orchestration of ML Workflows (22% of the exam content)

This domain accounts for 22% of the exam content.

Task 3.1: Select deployment infrastructure based on existing architecture and requirements

Knowledge of:

  • Deployment best practices (for example, versioning, rollback strategies)

  • deployment services (for example, SageMaker)

  • Methods to serve ML models in real time and in batches

  • How to provision compute resources in production environments and test environments (for example, CPU, GPU)

  • Model and endpoint requirements for deployment endpoints (for example, serverless endpoints, real-time endpoints, asynchronous endpoints, batch inference)

  • How to choose appropriate containers (for example, provided or customized)

  • Methods to optimize models on edge devices (for example, SageMaker Neo)

Skills in:

  • Evaluating performance, cost, and latency tradeoffs

  • Choosing the appropriate compute environment for training and inference based on requirements (for example, GPU or CPU specifications, processor family, networking bandwidth)

  • Selecting the correct deployment orchestrator (for example, Apache Airflow, SageMaker Pipelines)

  • Selecting multi-model or multi-container deployments

  • Selecting the correct deployment target (for example, SageMaker endpoints, Kubernetes, Amazon Elastic Container Service [Amazon ECS], Amazon Elastic Kubernetes Service [Amazon EKS], Lambda)

  • Choosing model deployment strategies (for example, real time, batch)

Task 3.2: Create and script infrastructure based on existing architecture and requirements

Knowledge of:

  • Difference between on-demand and provisioned resources

  • How to compare scaling policies

  • Tradeoffs and use cases of infrastructure as code (IaC) options (for example, CloudFormation, Cloud Development Kit [ CDK])

  • Containerization concepts and container services

  • How to use SageMaker endpoint auto scaling policies to meet scalability requirements (for example, based on demand, time)

Skills in:

  • Applying best practices to enable maintainable, scalable, and cost-effective ML solutions (for example, automatic scaling on SageMaker endpoints, dynamically adding Spot Instances, by using Amazon EC2 instances, by using Lambda behind the endpoints)

  • Automating the provisioning of compute resources, including communication between stacks (for example, by using CloudFormation, CDK)

  • Building and maintaining containers (for example, Amazon Elastic Container Registry [Amazon ECR], Amazon EKS, Amazon ECS, by using bring your own container [BYOC] with SageMaker)

  • Configuring SageMaker endpoints within the VPC network

  • Deploying and hosting models by using the SageMaker SDK

  • Choosing specific metrics for auto scaling (for example, model latency, CPU utilization, invocations per instance)

Task 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines

Knowledge of:

  • Capabilities and quotas for CodePipeline, CodeBuild, and CodeDeploy

  • Automation and integration of data ingestion with orchestration services

  • Version control systems and basic usage (for example, Git)

  • CI/CD principles and how they fit into ML workflows

  • Deployment strategies and rollback actions (for example, blue/green, canary, linear)

  • How code repositories and pipelines work together

Skills in:

  • Configuring and troubleshooting CodeBuild, CodeDeploy, and CodePipeline, including stages

  • Applying continuous deployment flow structures to invoke pipelines (for example, Gitflow, GitHub Flow)

  • Using services to automate orchestration (for example, to deploy ML models, automate model building)

  • Configuring training and inference jobs (for example, by using Amazon EventBridge rules, SageMaker Pipelines, CodePipeline)

  • Creating automated tests in CI/CD pipelines (for example, integration tests, unit tests, end-to-end tests)

  • Building and integrating mechanisms to retrain models