Anthropic Academy Courses

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Claude with Google Cloud's Vertex AI

This comprehensive course covers the full spectrum of working with Anthropic models through Google Cloud's Vertex AI.

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About this course

Course Description

This course provides comprehensive technical training on integrating and deploying Claude AI models through Google Cloud's Vertex AI. Developers will learn to implement Claude's API capabilities, from basic request handling to advanced features including tool use, retrieval augmented generation (RAG), and the Model Context Protocol (MCP). The curriculum covers practical implementation patterns, performance optimization techniques, and production-ready workflows for building AI-powered applications.

What you'll learn

  • Set up and configure Claude models through Google Cloud's Vertex AI
  • Implement multi-turn conversations with proper message handling and context management
  • Design and evaluate prompts using systematic testing workflows and automated grading techniques
  • Apply prompt engineering principles including XML tag structuring, example-based learning, and output control
  • Build tool-use implementations enabling Claude to interact with external functions and APIs
  • Develop RAG pipelines using text chunking, embeddings, BM25 search, and contextual retrieval techniques
  • Utilize advanced Claude features including vision capabilities, PDF processing, citation generation, and prompt caching
  • Implement the Model Context Protocol for creating custom tools, resources, and prompt templates
  • Configure and deploy Anthropic Apps including Claude Code for automated development tasks and Computer Use for UI automation
  • Design agent-based workflows with parallelization, chaining, and routing patterns for complex AI systems

Prerequisites

  • Proficiency in Python programming
  • Experience with Google Cloud Platform
  • Understanding of JSON data structures

Who this course is for

  • Backend developers building AI-powered APIs and services
  • Full-stack engineers integrating LLM capabilities into applications
  • ML engineers implementing production AI systems
  • DevOps professionals deploying and scaling Claude implementations
  • Technical architects designing AI-enhanced system architectures
  • Developers transitioning from other LLM providers to Claude
  • Engineers working on document processing, code generation, or automation workflows

Curriculum

  • Introduction
  • Welcome to the course
  • Anthropic overview
  • Overview of Claude models
  • Accessing Claude with the API
  • Accessing the API
  • Vertex AI Setup
  • Making a request
  • Multi-turn conversations
  • Chat exercise
  • System prompts
  • System prompts exercise
  • Temperature
  • Course satisfaction survey
  • Response streaming
  • Controlling model output
  • Structured data
  • Structured data exercise
  • Quiz on accessing Claude with the API
  • Prompt evaluation
  • Prompt evaluation
  • A typical eval workflow
  • Generating test datasets
  • Running the eval
  • Model based grading
  • Code based grading
  • Exercise on prompt evals
  • Quiz on prompt evaluation
  • Prompt engineering techniques
  • Prompt engineering
  • Being clear and direct
  • Being specific
  • Structure with XML tags
  • Providing examples
  • Exercise on prompting
  • Quiz on prompt engineering techniques
  • Tool use with Claude
  • Introducing tool use
  • Project overview
  • Tool functions
  • Tool schemas
  • Handling message blocks
  • Sending tool results
  • Multi-turn conversations with tools
  • Implementing multiple turns
  • Using multiple tools
  • The batch tool
  • Tools for structured data
  • The text edit tool
  • The web search tool
  • Quiz on tool use with Claude
  • Retrieval Augmented Generation
  • Introducing Retrieval Augmented Generation
  • Text chunking strategies
  • Text embeddings
  • The full RAG flow
  • Implementing the RAG flow
  • BM25 lexical search
  • A Multi-index RAG pipeline
  • Reranking results
  • Contextual retrieval
  • Quiz on Retrieval Augmented Generation
  • Features of Claude
  • Extended thinking
  • Image support
  • PDF support
  • Citations
  • Prompt caching
  • Rules of prompt caching
  • Prompt caching in action
  • Quiz on features of Claude
  • Model Context Protocol
  • Introducing MCP
  • MCP clients
  • Project setup
  • Defining tools with MCP
  • The server inspector
  • Implementing a client
  • Defining resources
  • Accessing resources
  • Defining prompts
  • Prompts in the client
  • MCP review
  • Quiz on Model Context Protocol
  • Anthropic apps - Claude Code and computer use
  • Anthropic apps
  • Claude Code setup
  • Claude Code in action
  • Enhancements with MCP servers
  • Parallelizing Claude Code
  • Automated debugging
  • Computer use
  • How computer use works
  • Agents and workflows
  • Agents and workflows
  • Parallelization workflows
  • Chaining workflows
  • Routing workflows
  • Agents and tools
  • Environment inspection
  • Workflows vs agents
  • Quiz on agents and workflows
  • Final assessment
  • Final assessment quiz
  • Wrapping up!
  • Course Wrap Up

About this course

Course Description

This course provides comprehensive technical training on integrating and deploying Claude AI models through Google Cloud's Vertex AI. Developers will learn to implement Claude's API capabilities, from basic request handling to advanced features including tool use, retrieval augmented generation (RAG), and the Model Context Protocol (MCP). The curriculum covers practical implementation patterns, performance optimization techniques, and production-ready workflows for building AI-powered applications.

What you'll learn

  • Set up and configure Claude models through Google Cloud's Vertex AI
  • Implement multi-turn conversations with proper message handling and context management
  • Design and evaluate prompts using systematic testing workflows and automated grading techniques
  • Apply prompt engineering principles including XML tag structuring, example-based learning, and output control
  • Build tool-use implementations enabling Claude to interact with external functions and APIs
  • Develop RAG pipelines using text chunking, embeddings, BM25 search, and contextual retrieval techniques
  • Utilize advanced Claude features including vision capabilities, PDF processing, citation generation, and prompt caching
  • Implement the Model Context Protocol for creating custom tools, resources, and prompt templates
  • Configure and deploy Anthropic Apps including Claude Code for automated development tasks and Computer Use for UI automation
  • Design agent-based workflows with parallelization, chaining, and routing patterns for complex AI systems

Prerequisites

  • Proficiency in Python programming
  • Experience with Google Cloud Platform
  • Understanding of JSON data structures

Who this course is for

  • Backend developers building AI-powered APIs and services
  • Full-stack engineers integrating LLM capabilities into applications
  • ML engineers implementing production AI systems
  • DevOps professionals deploying and scaling Claude implementations
  • Technical architects designing AI-enhanced system architectures
  • Developers transitioning from other LLM providers to Claude
  • Engineers working on document processing, code generation, or automation workflows

Curriculum

  • Introduction
  • Welcome to the course
  • Anthropic overview
  • Overview of Claude models
  • Accessing Claude with the API
  • Accessing the API
  • Vertex AI Setup
  • Making a request
  • Multi-turn conversations
  • Chat exercise
  • System prompts
  • System prompts exercise
  • Temperature
  • Course satisfaction survey
  • Response streaming
  • Controlling model output
  • Structured data
  • Structured data exercise
  • Quiz on accessing Claude with the API
  • Prompt evaluation
  • Prompt evaluation
  • A typical eval workflow
  • Generating test datasets
  • Running the eval
  • Model based grading
  • Code based grading
  • Exercise on prompt evals
  • Quiz on prompt evaluation
  • Prompt engineering techniques
  • Prompt engineering
  • Being clear and direct
  • Being specific
  • Structure with XML tags
  • Providing examples
  • Exercise on prompting
  • Quiz on prompt engineering techniques
  • Tool use with Claude
  • Introducing tool use
  • Project overview
  • Tool functions
  • Tool schemas
  • Handling message blocks
  • Sending tool results
  • Multi-turn conversations with tools
  • Implementing multiple turns
  • Using multiple tools
  • The batch tool
  • Tools for structured data
  • The text edit tool
  • The web search tool
  • Quiz on tool use with Claude
  • Retrieval Augmented Generation
  • Introducing Retrieval Augmented Generation
  • Text chunking strategies
  • Text embeddings
  • The full RAG flow
  • Implementing the RAG flow
  • BM25 lexical search
  • A Multi-index RAG pipeline
  • Reranking results
  • Contextual retrieval
  • Quiz on Retrieval Augmented Generation
  • Features of Claude
  • Extended thinking
  • Image support
  • PDF support
  • Citations
  • Prompt caching
  • Rules of prompt caching
  • Prompt caching in action
  • Quiz on features of Claude
  • Model Context Protocol
  • Introducing MCP
  • MCP clients
  • Project setup
  • Defining tools with MCP
  • The server inspector
  • Implementing a client
  • Defining resources
  • Accessing resources
  • Defining prompts
  • Prompts in the client
  • MCP review
  • Quiz on Model Context Protocol
  • Anthropic apps - Claude Code and computer use
  • Anthropic apps
  • Claude Code setup
  • Claude Code in action
  • Enhancements with MCP servers
  • Parallelizing Claude Code
  • Automated debugging
  • Computer use
  • How computer use works
  • Agents and workflows
  • Agents and workflows
  • Parallelization workflows
  • Chaining workflows
  • Routing workflows
  • Agents and tools
  • Environment inspection
  • Workflows vs agents
  • Quiz on agents and workflows
  • Final assessment
  • Final assessment quiz
  • Wrapping up!
  • Course Wrap Up