How to Define a Storage Infrastructure for AI and Analytical Workloads

Discover how to build scalable AI/ML data pipelines and select the optimal storage solutions for your specific use case in this 40-minute session. Learn to optimize AI/ML workloads including data preparation, training, tuning, inference, and serving by integrating the best storage solutions into Compute Engine, Google Kubernetes Engine, or Vertex workflows. Explore techniques for enhancing analytics workloads using Cloud Storage and Anywhere Cache. Gain insights from speakers David Stiver, Alex Bain, Jason Wu, and Yusuke Yachide on defining an effective storage infrastructure for AI and analytical workloads.

How to define a storage infrastructure for AI and analytical workloads