Harnessing Google Vertex AI Online Course
Harnessing Google Vertex AI Online Course
Gain expertise in Google Vertex AI’s Text-Embeddings API with this comprehensive course. Start by setting up your development environment and Google Cloud Platform, laying the groundwork for generating sentence embeddings. Explore the fundamentals of embeddings, their role in Generative AI and LLMs, and engage in hands-on exercises, including visualization and similarity searches. Learn to apply text and multimodal embeddings for real-world scenarios, from text generation to information extraction. By the end of the course, you’ll be equipped to build scalable AI solutions, such as a Retrieval-Augmented Generation (RAG) system, and effectively utilize Vertex AI in your projects.
Key Benefits
- Gain a comprehensive understanding of Google Vertex AI’s Text-Embeddings API and its capabilities.
- Learn to effectively configure and optimize your Google Cloud Platform environment for AI-driven applications.
- Develop practical skills through hands-on experience with embeddings, similarity searches, and text generation models.
Target Audience
This course is for data scientists, AI professionals, and developers seeking to enhance their expertise in Google Vertex AI. A fundamental understanding of Python and machine learning principles is recommended, while prior experience with cloud platforms and API integration will be advantageous for maximizing the learning experience.
Learning Objectives
- Set up and optimize your development environment for seamless integration with Google Vertex AI
- Generate and interpret embeddings while effectively visualizing their relationships
- Leverage text generation models for classification, summarization, and structured data extraction
- Implement advanced search techniques using cosine similarity and embeddings
- Develop practical AI-driven solutions by harnessing the full potential of Vertex AI
- Construct a Retrieval-Augmented Generation (RAG) system and analyze StackOverflow data for meaningful insights
Course Outline
The Harnessing Google Vertex AI Exam covers the following topics -
Module 1. Course Overview and Prerequisites
- Introduction to the Course and Learning Objectives
- Required Skills and Prerequisites for Effective Learning
Module 2. Setting Up the Development Environment & Google Cloud Platform
- Overview of Development Environment Configuration and API Cost Considerations
- Configuring Google Cloud for Vertex AI Integration
- Hands-on: Generating Sentence Embeddings Using Vertex AI for Initial Testing
Module 3. Deep Dive into Vertex AI Text Embedding API and Embeddings Fundamentals
- Understanding Vertex AI and Its Key Capabilities
- Optional: Foundational Concepts of Embeddings
- Applications of Embeddings in Generative AI and Large Language Models (LLMs)
- Exploring the Embeddings API: Text-Based vs. Multimodal Embeddings
- Key Use Cases, Task Types, and Advantages of Embeddings
- Visual Representation: Multimodal Embeddings Architecture
- Hands-on: Understanding Embedding Dimensions and Length
- Hands-on: Performing Cosine Similarity Search on Various Sentences
- Hands-on: Visualizing Embedding Representations
Module 4. Generating Text with Vertex AI Text Embedding API
- Implementing Text Generation Using the Bison Model
- Hands-on: Text Classification Use Case for Real-World Applications
- Hands-on: Extracting Structured Information into Tables and JSON Formats
- Hands-on: Controlling Model Temperature for Output Variability
- Hands-on: Adjusting Top-K and Top-P for Optimized Text Generation
- Hands-on: Summarizing and Extracting Key Information from Transcripts
Module 5. Practical Applications and Real-World Use Cases of Embeddings
- Visualizing Stack Overflow Q&A Clusters in a 2D Space
- Hands-on: Developing a Retrieval-Augmented Generation (RAG) System with Stack Overflow Data
- Scaling Search Performance Using Approximate Nearest Neighbor (ANN) Techniques: HNSW vs. Cosine Similarity
