Integrating Bedrock and SageMaker for Scalable AI Workflows

Modern AI development requires connecting powerful foundation models with robust machine learning pipelines. Bridging the gap between managed generative AI services and scalable ML infrastructure is essential for building production-grade applications.\n\nThis text-based course guides you through the process of integrating Bedrock with SageMaker. You will learn how to orchestrate workflows, manage features, and deploy scalable AI solutions using industry-standard patterns.\n\nWhat you'll learn:\n- Understand the foundational concepts of Bedrock foundation models and SageMaker infrastructure\n- Configure Lambda action groups to trigger automated tasks and integrate external APIs\n- Import custom models and manage features efficiently using SageMaker Feature Store\n- Build and orchestrate end-to-end AI workflows with SageMaker Pipelines\n- Apply retrieval-augmented generation (RAG) patterns and prompt engineering basics to enhance model outputs\n- Practice deploying and monitoring secure, scalable AI applications in a production environment\n\nStarting with key terminology and core architecture, the course walks you through step-by-step written tutorials, code examples, and architectural patterns to help you build reliable AI integrations. This course is designed for beginner to intermediate developers, data scientists, and cloud engineers who want to learn AI integration. Basic familiarity with cloud concepts and Python is helpful, but no prior machine learning experience is required.\n\nStart reading today to master the integration of foundation models and production machine learning workflows.