Production Machine Learning Systems

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 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.

• Introduction to Advanced Machine Learning on Google Cloud 3mins
• Introduction to Advanced Machine Learning on Google Cloud 3mins
• Architecting Production ML Systems 39mins
• Architecting Production ML Systems 35mins
• Designing Adaptable ML Systems 45mins
• Designing Adaptable ML Systems 45mins
• Designing High-Performance ML Systems 42mins
• Designing High-Performance ML Systems 42mins
• Building Hybrid ML Systems 20mins
• Building Hybrid ML Systems 20mins
• Summary 1min
• Summary 1min
• Course Resources 0mins
• Course Resources 0mins