# Content Domain 3: Deployment and Orchestration of ML and AI Workflows
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**Topics**
+ [Task 3.1: Manage deployment infrastructure for ML and AI model types.](#machine-learning-engineer-associate-02-domain3-task1)
+ [Task 3.2: Provision and configure resources for ML and AI workloads based on existing architecture and requirements.](#machine-learning-engineer-associate-02-domain3-task2)
+ [Task 3.3: Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads.](#machine-learning-engineer-associate-02-domain3-task3)

## Task 3.1: Manage deployment infrastructure for ML and AI model types.
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+ Skill 3.1.1: Select appropriate compute environments and deployment targets.
+ Skill 3.1.2: Select deployment orchestrators and multi-model or multi-container deployment strategies.
+ Skill 3.1.3: Select model inference strategies (for example, real-time and batch processing).
+ Skill 3.1.4: Evaluate and select appropriate foundation model (FM) deployment options.
+ Skill 3.1.5: Deploy models that were built outside of AWS into AWS environments (for example, Amazon SageMaker AI, Amazon Bedrock Custom Model Import).
+ Skill 3.1.6: Deploy and configure agents for specific tasks, integration with other services and tools, and agent communication protocols.
+ Skill 3.1.7: Configure FM deployment, model hosting, and resource allocation.
+ Skill 3.1.8: Apply Retrieval Augmented Generation (RAG) system configurations (for example, retrieval strategies, reranking).

## Task 3.2: Provision and configure resources for ML and AI workloads based on existing architecture and requirements.
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+ Skill 3.2.1: Optimize resource provisioning between on-demand and provisioned resources for performance and cost efficiency.
+ Skill 3.2.2: Automate compute resource provisioning with integrated communication between stacks and orchestration services.
+ Skill 3.2.3: Build and maintain containers for ML and AI workloads.
+ Skill 3.2.4: Configure SageMaker AI endpoints within VPC network environments.
+ Skill 3.2.5: Deploy and host models programmatically (for example, by using the SageMaker AI SDK for Python, AWS CLI, Boto3).
+ Skill 3.2.6: Select specific metrics for auto scaling implementations.
+ Skill 3.2.7: Create and manage Amazon Bedrock knowledge bases with vector database configurations, document indexing, and retrieval optimization.
+ Skill 3.2.8: Implement retrieval pipelines to meet business needs.
+ Skill 3.2.9: Implement agent state management systems.
+ Skill 3.2.10: Implement AI-specific resource scaling for GPU workloads.
+ Skill 3.2.11: Deploy agentic workflow infrastructure.

## Task 3.3: Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads.
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+ Skill 3.3.1: Implement automated deployment strategies and rollback actions.
+ Skill 3.3.2: Configure and troubleshoot AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, AWS CodePipeline, and AWS CodeConnections.
+ Skill 3.3.3: Configure training and inference jobs.
+ Skill 3.3.4: Configure automated testing strategies within CI/CD pipelines for traditional ML and AI workloads.
+ Skill 3.3.5: Build and integrate mechanisms to re-train models.
+ Skill 3.3.6: Manage model versions for repeatability and audits (for example, SageMaker Model Registry, MLflow on SageMaker AI).
+ Skill 3.3.7: Manage prompts (for example, Amazon Bedrock Prompt Management).
+ Skill 3.3.8: Implement automated agent deployment pipelines and agent version management.
+ Skill 3.3.9: Implement AI model testing frameworks, including prompt testing.
+ Skill 3.3.10: Configure FM deployment automation with fine-tuned model versioning.
+ Skill 3.3.11: Configure AI-specific pipeline orchestration for RAG system updates and knowledge base refresh cycles.

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for AWS Certification. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query aws-certification` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).