AWS Bedrock Complete Guide: Foundation Models, Agents

This is a brief introduction to why this course for all learners aspiring to learn AWS Bedrock , this is stand out course to cover quantity topics over quality , having worked in Bedrock now for more than 4 years , it is key to get end - end perspective to the service which can help to build solution for customers. 

Protocol for the course ( what to expect)

a) We will broadly provide the concept of the lecture in the lesson via video

b) There would be one or more links which can be used to understand the lecture in detail

c) No labs for students in the course , would be providing wherever applicable links to github or workshops for the lecture , you can use the same for practicing or understanding the topic more in detail

d) this is a breath or broad course than focussed , as each of the lecture can be a course in the next series, this is one course which must be gone thru by all

e) Resources section and Bedrock 101  Linkedin Group can be enrolled for more details

f) The course also covers some of the valuable resources which can be used by practitioner and also certification linked in to the topic and also SkillBuilder which is the OEM digital platform for learning

This course,  introduces Amazon Bedrock, a fully managed AWS service for building generative AI applications by providing access to leading foundation models (FMs) through a single API. These courses focus on using Bedrock's features, like model customization, Retrieval-Augmented Generation (RAG) (also known as RAG), agent orchestration, and integration with AWS services, to develop and scale AI-powered solutions securely and efficiently

Working with Bedrock involves using and selecting the right foundation models for the project , in this Lecture we will cover the various foundation models provided by Bedrock and how to chose them and also the foundation models provides from marketplace such as DeepSeek

The key best practices while building solutions with Amazon Bedrock is to leverage - Solution Library , Well Architected Generative AI Lenses , Prescriptive Guidance , in this lecture we will briefly cover these important resources

One of key requirement while working on projects is to augment enterprise data to the foundation models  by using KnowledgeBase (RAG) provided by Bedrock and also how to use Agents for orchestration of models.

In this video we will cover prompts and prompt engineering foundation and also a link to workshop to practice and understand prompt engineering in detail , In this comprehensive workshop, you'll gain the skills and knowledge needed to design, test, and refine prompts that optimize Anthropic Claude's performance for your specific use case. This workshop will guide you through a structured, test-driven approach to prompt development, equipping you with the tools and techniques to achieve unparalleled results.

In this lecture we will cover , how to  connect programmatically to an AWS service, you use an endpoint and also AWS software development kits (SDKs) are available for many popular programming languages. Each SDK provides an API, code examples, and documentation that make it easier for developers to build applications in their preferred language

Amazon Q family is a integral component of amazon generative solutions , Amazon Q is an AI Assistant and Q Developer and Q Business help in building rapid application development , In this lecture we will understand how we can integrate Q and Bedrock

In this Lecture we will cover the various tenants of Bedrock Security from standard security with AWS Security services like IAM, KMS and then specific functionality provided within Bedrock for hallucination, watermark and also API level security services

In this lecture we will briefly cover bedrock pricing - Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

Amazon Bedrock offers flexible pricing options to support customers at every stage of their generative AI journey. Customers can choose from on-demand pricing for pay-as-you-go usage with no upfront commitments, or batch mode for cost-efficient processing of large volumes of input. For high-volume and predictable workloads, provisioned throughput provides dedicated model capacity with discounted pricing. These options help optimize cost while balancing speed, scale, and model access needs.

In this lecture we will cover Amazon Nova and also the new ACT model , Amazon Nova models deliver frontier intelligence and industry-leading price-performance. With the most comprehensive suite of customization capabilities for any proprietary model family, organizations can customize Nova to deliver responses that reflect their industry expertise. Through Amazon Bedrock, organizations can seamlessly build and scale generative AI applications with Nova that are safe, reliable, and cost-effective. Get started today with the Amazon Nova user guide.

In this lecture we will cover agents and MCP - AI agents extend large language models (LLMs) by interacting with external systems, executing complex workflows, and maintaining contextual awareness across operations. Amazon Bedrock Agents enables this functionality by orchestrating foundation models (FMs) with data sources, applications, and user inputs to complete goal-oriented tasks through API integration and knowledge base augmentation. However, in the past, connecting these agents to diverse enterprise systems has created development bottlenecks, with each integration requiring custom code and ongoing maintenance—a standardization challenge that slows the delivery of contextual AI assistance across an organization’s digital ecosystem. This is a problem that you can solve by using Model Context Protocol (MCP), which provides a standardized way for LLMs to connect to data sources and tools.

In this lecture we will cover briefly the Foundation Model Reasoning Models and Frontier Reasoning

In this lecture we will cover Bedrock Data Automation , Automate the generation of useful insights from unstructured multimodal content such as documents, images, audio, and video for your AI-powered applications.

In this lecture we will cover , Amazon SageMaker Unified Studio is a single data and AI development environment where you can find and access all of the data in your organization and act on it using the best tools across any use case. SageMaker Unified Studio brings together the functionality and tools from existing AWS Analytics and AI/ML services, including Amazon EMR, AWS Glue, Amazon Athena, Amazon Redshift, Amazon Bedrock, and Amazon SageMaker AI

In this lecture we will cover bedrock flows, Learn how to leverage the flexibility and power of Bedrock Flows to accelerate your AI application development and deployment without writing code.

In this lecture we will cover Re-ranker model , Amazon Bedrock provides access to reranker models that you can use when querying to improve the relevance of the retrieved results. A reranker model calculates the relevance of chunks to a query and reorders the results based on the scores that it calculate

In this lecture we will cover chunking , When ingesting your data, Amazon Bedrock first splits your documents or content into manageable chunks for efficient data retrieval. The chunks are then converted to embeddings and written to a vector index (vector representation of the data), while maintaining a mapping to the original document. The vector embeddings allow the texts to be quantitatively compared.

In this lecture we will cover a important topic on Bedrock Troubleshooting which can occur while model customization  and also discuss the API error codes

In this lecture we will briefly cover about bedrock and LLMOPS - Large language models (LLMs) are a class of FMs that focus on language-based tasks such as summarization, text generation, classification, Q&A, and more. Large Language Model Operations (LLMOps), a subset of Foundation Model Operations (FMOps), focuses on the processes, techniques, and best practices used for the operational management of LLMs. Using LLMOps improves the efficiency in which models are developed and enables scalability to manage multiple models. FMOps stems from the concept of Machine Learning Operations (MLOps), which is the combination of people, processes, and technology to deliver machine learning solutions efficiently into production. It takes the MLOps methodology and adds the additional skills, processes, and technologies needed to operationalize generative AI models and applications.

In this lecture we will cover Prompt Optimization and Intelligent Routing , Amazon Bedrock Intelligent Prompt Routing routes prompts to different foundational models within a model family, helping you optimize for quality of responses and cost. Intelligent Prompt Routing can reduce costs by up to 30% without compromising on accuracy.

In this lecture we will cover LLM as Judge , The evaluation of large language model (LLM) performance, particularly in response to a variety of prompts, is crucial for organizations aiming to harness the full potential of this rapidly evolving technology. The introduction of an LLM-as-a-judge framework represents a significant step forward in simplifying and streamlining the model evaluation process. This approach allows organizations to assess their AI models’ effectiveness using pre-defined metrics, making sure that the technology aligns with their specific needs and objectives.

In this lecture we will discuss monitroing of Amazon Bedrock - You can monitor all parts of your Amazon Bedrock application using Amazon CloudWatch, which collects raw data and processes it into readable, near real-time metrics. You can graph the metrics using the CloudWatch console. You can also set alarms that watch for certain thresholds, and send notifications or take actions when values exceed those thresho

In this lecture , we will cover Model Distillation - With Amazon Bedrock Model Distillation, you can use smaller, faster, more cost-effective models that deliver use-case specific accuracy that is comparable to the most advanced models in Amazon Bedrock. Distilled models in Amazon Bedrock are up to 500% faster and up to 75% less expensive than original models, with less than 2% accuracy loss for use cases like RAG.

The most important development today is Vibe Coding which help developers generate code on fly with prompt and voice and enhance productivity and in this lecture we will brief about vibe coding and also a link to a workshop to understand ,  Learn how to build AI agents from rapid prototype to production deployment using Kiro. You'll learn to create powerful agents that can access AWS documentation, generate architectural diagrams, and provide expert guidance through integration with Model Context Protocol (MCP) servers and the Strands Agents SDK. The workshop culminates in deploying your agent to Amazon Bedrock AgentCore for scalable, enterprise-grade operation with built-in monitoring and security.

In this lecture , we will cover the open standards , and how developers can build interconnected generative AI applications through new services, open source contributions, and best practices. Also how to use the Strands Agents, an open source SDK for building and running AI agents in just a few lines of code. Strands supports various models through integrations with Amazon Bedrock, Anthropic API, Llama API, Ollama, and others via LiteLLM

In this lecture we will briefly cover , Amazon Bedrock AgentCore enables you to deploy and operate highly capable AI agents securely, at scale. It offers infrastructure purpose-built for dynamic agent workloads, powerful tools to enhance agents, and essential controls for real-world deployment. AgentCore services can be used together or independently and work with any framework including CrewAI, LangGraph, LlamaIndex, and Strands Agents, as well as any foundation model in or outside of Amazon Bedrock, giving you ultimate flexibility. AgentCore eliminates the undifferentiated heavy lifting of building specialized agent infrastructure, so you can accelerate agents to production and also suggest some workshop to practice the same

This lecture is to debrief the entire course . a) Resources Section b) Please leverage the workshop resources (workshops.aws) and understand the solution c) Use the best practices services ,  Well Architected Course d) AWS Certification - AIP and MLEA