Overview
Master Google Cloud Vertex AI: Harness LLMs and Text-Embeddings API to Build Advanced AI Solutions and Drive Insights
Syllabus
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- Introduction to Vertex AI
-- Overview of Vertex AI
-- Key features and capabilities
-- Setting up your Google Cloud environment
- Understanding Large Language Models (LLMs)
-- Introduction to LLMs
-- How LLMs work
-- Use cases of LLMs in industry
- Working with Text-Embeddings API
-- Introduction to text embeddings
-- Use cases for text embeddings
-- How to integrate Text-Embeddings API in projects
- Building with Vertex AI
-- Creating and managing datasets
-- Training and deploying machine learning models
-- Using pre-trained models and AutoML
- Advanced Vertex AI Capabilities
-- Hyperparameter tuning
-- Model evaluation and validation
-- Monitoring and optimization
- Leveraging LLMs within Vertex AI
-- Integrating LLMs into machine learning workflows
-- Customizing LLMs for specific tasks
-- Using LLMs for natural language processing tasks
- Real-world Applications
-- Developing AI solutions with case studies
-- Building chatbots and conversational agents
-- Sentiment analysis and content recommendation systems
- Security and Responsible AI
-- Ensuring data privacy and compliance
-- Ethical considerations in AI and LLM use
-- Best practices for deploying AI responsibly
- Capstone Project
-- Designing a comprehensive AI solution using Vertex AI
-- Incorporating LLMs and Text-Embeddings API
-- Presenting and evaluating project outcomes
- Next Steps and Resources
-- Advanced topics and further reading
-- Joining the Vertex AI community
-- Continuous learning opportunities and certifications
Taught by
Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor
Tags
On-Demand
