Projects
- Use a real-world data set to build a project in Vertex AI.
- Train, register, and deploy a machine learning (ML) model using Vertex AI.
- Keep track of data history and version, ensure resource shutdown, and monitor an ML model for performance and quality.
Learn how to train and manage machine learning models using Vertex AI, the MLOps cloud solution designed by Google. Join instructors Archana Vaidheeswaran and Soham Chatterjee as they show you the technical skills you need to know to build, train, register, deploy, and manage your own customizable ML model.
Learn how to use Vertex AI to:
- Build accurate, reliable, and scalable ML models, from loading data to feature engineering, hyperparameter tuning, and model deployment and monitoring.
- Store, organize, and collaborate on the measurable data properties of an ML model in the Vertex AI Feature Store.
- Train and evaluate models for a project with AutoML.
- Manage and version ML models, deploy them to production, and control access to them with the Vertex AI Model Registry.
- Deploy a trained deep learning model using Vertex AI Predictions.
- Visualize and analyze metrics of trained models with TensorBoard.
- Monitor models for performance and quality, cost and resource management, and model drift and feature attribution.
