Member since 2026
[DEPRECATED] How to Use TPUs for Inference
Earned Aug 5, 2026 EDT
[DEPRECATED] Architecting an AI Inference Stack
Earned Aug 1, 2026 EDT
Production Machine Learning Systems
Earned Jul 22, 2026 EDT
Build a Certification Study Guide: PMLE
Earned Jan 26, 2026 EST
This course is for developers interested in learning how to use TPUs for inference—from architecture to deployment, and how to solve common implementation challenges. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
This course is designed for developers looking to build an optimized AI inference stack on Google Cloud. Whether you’re working with GPUs or TPUs, you’ll explore the fundamental components of an inference stack, learn design principles for maximizing performance and reliability, and explore practical techniques to take your workloads from 0 to 1. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.