Vertex AI Training Course

Introduction to Vertex AI and Machine Learning Platforms

• Overview of AI and machine learning workflows
• Familiarization with Google Cloud Vertex AI
• Analyzing Vertex AI architecture and its key components
• Examining the role of Vertex AI in the development and deployment of ML models

Configuring the Vertex AI Environment

• Setting up a Google Cloud project for Vertex AI
• Understanding workspaces, resource allocation, and permission structures
• Preparing datasets and establishing development environments
• Navigating the Vertex AI interface and tools

Machine Learning Fundamentals with Vertex AI

• Exploring supervised learning methodologies
• Overview of regression and classification models
• Data preparation techniques for ML workflows
• Assessing model performance and accuracy metrics

Natural Language Processing (NLP) with Vertex AI

• Introduction to NLP principles
• Understanding text-based ML applications
• Techniques for preparing and processing textual data
• Discovering NLP features available within Vertex AI

Building and Training Machine Learning Models

• Writing training code optimized for Vertex AI
• Containerizing ML training applications
• Setting up training job configurations
• Executing and monitoring model training processes

Deploying Machine Learning Models

• Understanding deployment workflows
• Creating and managing model endpoints
• Deploying trained models for live predictions
• Managing resources associated with deployed models

Monitoring and Troubleshooting Vertex AI Solutions

• Tracking training and deployment activities
• Identifying and resolving common configuration errors
• Debugging model execution issues
• Implementing best practices for stable ML operations

Practical Workshop and Course Review

• Constructing an end-to-end machine learning workflow using Vertex AI
• Training and deploying a representative sample model
• Reviewing essential Vertex AI features and capabilities
• Discussing pathways for advanced ML development