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.Read more.
This resource is offered by an affiliate partner. If you pay for training, we may earn a commission to support this site.
The techniques and tools covered in Production Machine Learning Systems are most similar to the requirements found in Data Scientist job advertisements.