Building Smart Agents with Vertex AI Agent Builder & RAG Training Course
Vertex AI Agent Builder serves as a no-code/low-code platform designed for constructing grounded agents that merge generative models with retrieval-augmented generation (RAG). This enables teams to swiftly develop agents capable of leveraging enterprise data and search capabilities to deliver precise, context-sensitive responses.
This instructor-led, live training session, available either online or onsite, is tailored for intermediate-level professionals looking to design, configure, and deploy smart agents by utilizing Vertex AI Agent Builder alongside RAG frameworks.
Upon completing this training, participants will be equipped to:
- Architect grounded agent workflows using Agent Builder.
- Establish RAG pipelines incorporating search and vector stores.
- Securely integrate enterprise data sources for retrieval purposes.
- Assess and refine agent performance through testing and metric analysis.
Course Delivery Format
- Engaging lectures coupled with interactive discussions.
- Practical, hands-on labs utilizing Vertex AI Agent Builder and RAG components.
- Project-based activities focused on constructing and polishing agents.
Customization Opportunities
- For bespoke training arrangements, please reach out to us to discuss.
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder capabilities
- RAG fundamentals and strategic application
- Use cases and success stories
Setting Up the Environment
- Configuring Vertex AI workspace
- Connecting search and vector stores
- Hands-on lab: environment preparation
Designing Grounded Agent Workflows
- Defining agent goals and conversation flows
- Mapping data sources to retrieval strategies
- Hands-on lab: building a conversation flow
Implementing RAG Pipelines
- Indexing documents and embeddings
- Retriever and re-ranker patterns
- Hands-on lab: creating a RAG pipeline
Integrations and Enterprise Data
- Secure connectors to internal systems
- Data governance and access controls
- Hands-on lab: connecting enterprise data sources
Testing, Evaluation, and Iteration
- Prompt testing and evaluation metrics
- User simulation and validation strategies
- Hands-on lab: evaluating and tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment options and scaling considerations
- Monitoring performance, relevance, and drift
- Operational playbooks for updates and rollback
Summary and Next Steps
Requirements
- Fundamental understanding of natural language processing
- Hands-on experience with cloud services and APIs
- Knowledge of search mechanisms and vector databases
Target Audience
- Software Developers
- Solution Architects
- Product Managers
Open Training Courses require 5+ participants.
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