What you'll learn

AI and ML initiatives scatter training data, features, and embeddings across clouds and data centers, creating surprise costs, governance gaps, and new risks. In this course, Multi-Cloud Data Strategies for AI and ML Workloads, you’ll gain the ability to design multi-cloud data strategies that support AI and ML workloads. First, you’ll explore how vector stores and feature stores operate across distributed cloud sources. Next, you’ll discover how to govern AI and ML data pipelines that span clouds and how to evaluate the cost trade-offs between on-premises training and cloud-based inference. Finally, you’ll learn how to identify the data strategy risks specific to multi-cloud AI workloads and turn them into a defensible strategy. When you’re finished with this course, you’ll have the skills and knowledge of multi-cloud AI data strategy needed to design data strategies that support AI and ML workloads without losing control of cost or governance.