This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project based on the different goals of users, including data scientists, AI developers, and ML engineers.
Overview
Syllabus
- Introduction
- Course introduction
- AI Foundations
- Introduction
- Why Google
- Ai/ML framework on Google Cloud
- Google Cloud infrastructure
- Data and AI products
- ML model categories
- BigQuery ML
- Lab introduction
- Predicting Visitor Purchases with BigQuery ML
- Summary
- Quiz
- Reading
- AI Development Options
- Introduction
- AI developement options
- Pre-trained APIs
- Vertex AI
- AutoML
- Custom training
- Lab introduction
- Entity and Sentiment Analysis with the Natural Language API
- Summary
- Quiz
- Reading
- AI Development Workflow
- Introduction
- How a machine learns
- ML workflow
- Data preparation
- Model development
- Model serving
- MLOps and workflow automation
- Lab introduction
- Vertex AI: Predicting Loan Risk with AutoML
- Summary
- Quiz
- Reading
- Generative AI
- Introduction
- Generative AI and LLMs
- Generative AI use case: Duet AI
- Model Garden
- Generative AI Studio
- AI solutions
- Lab introduction
- Get Started with Vertex AI Studio
- Summary
- Quiz
- Reading
- Summary
- Course summary
- Reading
- Your Next Steps
- Course Badge