Production Machine Learning Systems

In this course, we dive into the components and best practices of building high-performing ML systems in production environments. We cover some of the most common considerations behind building these systems, e.g. static training, dynamic training, static inference, dynamic inference, distributed TensorFlow, and TPUs. This course is devoted to exploring the characteristics that make for a good ML system beyond its ability to make good predictions.

• Compare static versus dynamic training and inference
• Manage model dependencies
• Set up distributed training for fault tolerance, replication, and more
• Export models for portability