What you'll learn

Managing ML data and models across teams requires the right tools and workflows to ensure consistency, security, and reproducibility. In this course, (GCP-PMLE) Collaborating within and across Teams to Manage Data and Models, you'll gain the ability to efficiently organize and preprocess data for ML training while enabling team collaboration. First, you'll explore how to organize different data types across Google Cloud storage services, manage datasets in Vertex AI, and implement preprocessing pipelines using Dataflow and BigQuery. Next, you'll discover how to prototype models using Jupyter notebooks on Google Cloud, choosing the right backend for your needs and applying security best practices. Finally, you'll learn how to track and run ML experiments using Vertex AI Experiments, evaluate generative AI solutions, and leverage foundational models from Model Garden. When you're finished with this course, you'll have the skills and knowledge needed to effectively manage data and models in collaborative ML environments on Google Cloud.