In this chapter, we've looked at what it takes to bring deep learning and machine learning models to production. We've explored some of the basic principles that will help you to be successful with deep learning in a continuous delivery environment.
We've taken a look at exporting models to ONNX to make it easier to deploy your trained models to production and keep them running for years, thanks to the portable nature of the ONNX format. We then explored how you can use the CNTK API in other languages, such as C#, to make predictions.
Finally, we've looked at using Azure Machine Learning service to level-up your DevOps experience with experiment management, model management, and deployment tools. Although you don't need a tool like this to get started, it really helps to have something like Azure Machine Learning service in your arsenal when...
