Model Deployment and Responsible AI

This unit covers selecting appropriate ML techniques and AWS services, designing effective prompts for text and multimodal models, and deploying models with automated MLOps and inference tools. Learners will also apply explainability, fairness, and safety practices (SageMaker Clarify and Bedrock guardrails) and use Bedrock agents to operationalize model-based workflows, preparing them for advanced production and evaluation tasks in the final unit.

Learning Objectives

• Analyze real-world use cases to select appropriate machine learning techniques and AWS ML services (e.g., SageMaker, Bedrock) and justify choices based on performance, cost, and data type constraints
• Apply prompt engineering methods to design, test, and refine prompts for text and multimodal models and measure improvements using objective criteria (e.g., accuracy, relevance, latency)
• Demonstrate deployment workflows by configuring SageMaker inference endpoints, implementing Bedrock agents, and automating MLOps pipelines to make reproducible predictions from trained models
• Evaluate model fairness, explainability, and safety by using SageMaker Clarify and Bedrock guardrails to identify biases or risks and propose concrete remediation strategies