Google Vertex AI for Machine Learning Practitioners

Machine Learning Engineers, Data Scientists

By the end of the course, learners will be able to:

• Understand the key components of Vertex AI and how they work together to support ML workflows.
• Configure and launch Vertex AI Custom Training and Hyperparameter Tuning jobs to optimize model performance.
• Organize and version models using Vertex AI Model Registry for easy access and tracking.
• Configure serving clusters and deploy models for online predictions with Vertex AI Endpoints.
• Operationalize and orchestrate end-to-end ML workflows with Vertex AI Pipelines for increased efficiency and scalability.
• Configure and set up monitoring on deployed models.

Module 1: Training, Tuning, and Deploying Models on Vertex AI

• Understand Containerized Training Applications
• Understand Vertex AI Custom Training and Tuning Jobs
• Understand how to track and version your trained models in the Vertex AI Model Registry
• Understand Online Deployment with Vertex AI Endpoints

Module 2: Orchestrating End-to-End Workflows with Vertex AI Pipelines

• Understand Kubeflow
• Understand pre-built and lightweight Python components
• Understand how to compile and execute pipelines on Vertex AI

Module 3: Model Monitoring on Vertex AI

• Understand Feature Drift and Skew
• Understand Model Monitoring for models deployed to Vertex AI Endpoints

• Experience building and training custom ML models. Familiar with Docker.

• None

• None recommended

• Official course book provided to participants