This module introduces agentic AI by tracing its evolution from traditional large language models (LLMs). This historical context sets the stage for a deeper understanding of agentic AI. Agents build on key technologies that overcome the limitations of large language models (LLMs).

Amazon Bedrock AgentCore Memory provides a managed service that enables AI agents to maintain context over time, remember important facts, and deliver consistent, personalized experiences. In this course you will follow a series of tutorials to learn how to use Amazon Bedrock AgentCore Memory.

Amazon Bedrock AgentCore Observability helps developers trace, debug, and monitor agent performance in production through unified operational dashboards. In this course you will follow a series of tutorials to learn how to use Amazon Bedrock AgentCore Observability.

Amazon Lex is a fully managed AI service with advanced natural language models to design, build, test, and deploy conversational interfaces for voice and text. Learn about the benefits, features, typical use cases, and technical concepts.

Amazon Nova Act SDK is a powerful software development kit that enables developers to build intelligent action-based applications using the advanced AI capabilities of AWS. Learn fundamental concepts, core functionality, and practical implementation strategies.

Designed specifically for learners in China/Hong Kong. Aimed at technicians who want to develop their knowledge and skills in AWS AI services and solutions with practical examples and hands-on exercises.

Covers the data preparation phase of the ML lifecycle. Learn about data fundamentals, AWS storage services like Amazon S3, and services that assist with ingesting, extracting, and merging data.

Explore the AWS ML stack layers and learn how to solve common business challenges with AWS services. Explore Amazon SageMaker and strategies for selecting appropriate models.

Delve into the core elements of the model training process and learn how to select the most suitable compute environment. Explore SageMaker pre-built deep learning framework containers.

Learn how to refine ML models. Review methods for bias mitigation and model performance, prevent overfitting and underfitting, and use hyperparameter tuning to produce optimized results.

Shows how to enhance existing applications with powerful AI capabilities. Discover how to integrate AI features using Amazon Bedrock and Q Developer with hands-on labs and exercises.

Learn how to design, build, and deploy Claude-powered agents using Amazon Bedrock. Explore action groups, knowledge bases, guardrails, and integration with Amazon Connect and Lex V2.

Explore real-world AI, ML and generative AI use cases across healthcare, finance, marketing, entertainment, and more. Learn about capabilities, limitations, and model selection techniques.

Learn about the foundations of ML and AI. Explore connections between AI, ML, deep learning, and generative AI. Gain practical insights into AWS services that use AI and ML capabilities.

Explore RAG and fine-tuning techniques to improve foundation model performance. Learn about vector databases, the role of agents in multi-step tasks, and data preparation for fine-tuning.

Comprehensive journey to understand and implement responsible AI practices using AWS technologies. Covers fairness, explainability, privacy, robustness, governance, and transparency.

Learn about responsible AI practices including core dimensions, services and tools AWS offers, model selection considerations, and transparent and explainable models.

Understand common security, compliance, and governance issues for AI solutions. Learn about governance controls, compliance objectives, and AWS services to secure AI systems.