Keras to Kubernetes

CHAPTER 9Machine Learning Development Lifecycle

In the previous chapter, we deployed an AI model packaged with a web application that responded to HTTP requests. We saw how we developed, trained, and validated this model with sample data. Then this model was deployed inside a web application packaged together as a Docker container. This container was then deployed on Kubernetes as a microservice with the platform, providing infrastructure features like scaling, fail‐over, and load balancing. This approach is highly customized and needs tight coupling between the application code and the model. Software engineers need to know exactly how to call the model and need to manage the runtime for the model. A better approach is to deploy the model as an independent microservice and let the application call this microservice with agreed‐upon lightweight protocols. This way, the application has its own development lifecycle and the model has its own. This Machine Learning development lifecycle is gaining lot of popularity in the industry.

We will talk about the steps involved in the Machine Learning development lifecycle. We will talk about some best practices used by data scientists around different steps in working on a data science problem like data collection, cleansing, and structuring. We will explore policies for selecting the best modeling technique based on the type of data and the problem being solved. Finally, we will talk about deployment of the model in production both on ...