Vertex AI Training Course

Introduction to Vertex AI and Machine Learning Platforms

• An overview of artificial intelligence and machine learning workflows
• An introduction to Google Cloud's Vertex AI
• Understanding the architecture and key components of Vertex AI
• Exploring Vertex AI's role in machine learning development and deployment

Setting Up the Vertex AI Environment

• Configuring a Google Cloud project specifically for Vertex AI
• Understanding workspaces, resource allocation, and permission management
• Preparing datasets and setting up development environments
• Navigating Vertex AI tools and user interfaces

Machine Learning Fundamentals with Vertex AI

• Comprehending supervised learning concepts
• An overview of regression and classification models
• Data preparation strategies for machine learning workflows
• Evaluating model performance and accuracy

Natural Language Processing (NLP) with Vertex AI

• Introduction to core NLP concepts
• Understanding text-based machine learning applications
• Techniques for preparing and processing text data
• Exploring the specific NLP capabilities offered within Vertex AI

Building and Training Machine Learning Models

• Preparing training code compatible with Vertex AI
• Containerizing machine learning training applications
• Configuring training jobs effectively
• Running and monitoring the model training process

Deploying Machine Learning Models

• Understanding the workflows involved in model deployment
• Creating model endpoints
• Deploying trained models to serve predictions
• Managing deployed models and associated resources

Monitoring and Troubleshooting Vertex AI Solutions

• Monitoring training and deployment activities
• Identifying common configuration issues
• Troubleshooting problems related to model execution
• Applying best practices to ensure reliable ML workflows

Practical Workshop and Course Review

• Constructing a complete machine learning workflow using Vertex AI
• Training and deploying a sample model
• Reviewing key Vertex AI features and capabilities
• Discussing next steps for advanced machine learning development