Vertex AI for Machine Learning Practitioners

• Understand the key components of Vertex AI and how they work together to support your ML workflows.
• Configure and launch Vertex AI Custom Training and Hyperparameter Tuning Jobs to optimize model performance.
• Organize and version your 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

Machine Learning Engineers, Data Scientists

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

Module 01 : Training, Tuning, and Deploying Models on Vertex AI

Topics

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

Activities

• Lab: Train, Tune, and Serve Models with Vertex AI

Module 02 : Orchestrating end-to-end Workflows with Vertex AI Pipelines

Topics

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

Activities

• Lab: Orchestrating an AutoML Based Pipeline with Vertex AI Pipelines
• Lab: Orchestrate Custom Training and Tuning Pipeline with Pre-Built Components
• Lab: Orchestrate Custom Training and Tuning Pipeline with Lightweight Python Components

Module 03 : Model Monitoring on Vertex AI

Topics

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

Activities

• Lab: Vertex AI Model Monitoring