Vertex AI for Machine Learning Practitioners
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Learning Track
Machine Learning and Artificial Intelligence
Delivery methods
On-Site, Virtual
Duration
1 day
This instructor-led one-day course is designed for engineers and data scientists familiar with machine learning models who want to become proficient in using Vertex AI for custom model workflows. This practical, hands-on course will provide you with a deep dive into the core functionalities of Vertex AI, enabling you to effectively leverage its tools and capabilities for your ML projects.
Course objectives
- 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
Audience
- Machine Learning Engineers, Data Scientists
Prerequisites
- Experience building and training custom ML models. Familiar with Docker.
Course outline
- 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
- Understand Kubeflow
- Understand pre-built and lightweight Python components
- Understand how to compile and execute pipelines on Vertex AI
- Understand Feature Drift and Skew
- Understand Model Monitoring for models deployed to Vertex AI Endpoints
