Building Smart Agents with Vertex AI Agent Builder & RAG Training Course
Vertex AI Agent Builder provides a no-code/low-code platform for constructing grounded agents that integrate generative models with Retrieval-Augmented Generation (RAG). This enables teams to swiftly develop intelligent agents capable of leveraging enterprise data and search capabilities to deliver precise, context-aware responses.
This live, instructor-led training (available online or onsite) targets intermediate-level professionals looking to design, configure, and deploy smart agents utilizing Vertex AI Agent Builder and RAG architectures.
Upon completion of this course, participants will be able to:
- Construct grounded agent workflows using Agent Builder.
- Build RAG pipelines incorporating search and vector storage solutions.
- Securely integrate enterprise data sources for data retrieval.
- Assess and refine agent performance through testing and key metrics.
Course Format
- Interactive lectures and discussions.
- Practical labs utilizing Vertex AI Agent Builder and RAG components.
- Project-based exercises focused on building and optimizing agents.
Customization Options
- For information on arranging a customized training session for this course, please contact us.
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder capabilities.
- RAG fundamentals and appropriate use cases.
- Real-world use cases and success stories.
Environment Setup
- Configuring the Vertex AI workspace.
- Establishing connections to search and vector stores.
- Hands-on lab: Preparing the development environment.
Designing Grounded Agent Workflows
- Defining agent objectives and conversation flows.
- Mapping data sources to retrieval strategies.
- Hands-on lab: Constructing a conversation flow.
Implementing RAG Pipelines
- Document indexing and embedding creation.
- Implementing retriever and re-ranker patterns.
- Hands-on lab: Building a RAG pipeline.
Integrations and Enterprise Data
- Secure connectivity to internal systems.
- Data governance and access control mechanisms.
- Hands-on lab: Connecting to enterprise data sources.
Testing, Evaluation, and Iteration
- Prompt testing and evaluation metrics.
- Strategies for user simulation and validation.
- Hands-on lab: Evaluating and tuning agent performance.
Deployment, Monitoring, and Maintenance
- Deployment options and scaling considerations.
- Monitoring performance, relevance, and drift.
- Operational playbooks for updates and rollback procedures.
Summary and Next Steps
Requirements
- Fundamental understanding of natural language processing.
- Practical experience with cloud services and APIs.
- Familiarity with search engines and vector databases.
Audience
- Software Developers
- Solution Architects
- Product Managers
Open Training Courses require 5+ participants.
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