This technical blog series titled “Reproducibility in Machine Learning” is going to be divided into three parts:

1. Reproducibility in Machine Learning - Research and Industry
2. Realizing reproducible Machine Learning - with Tensorflow
3. End-to-end reproducible Machine Learning pipelines on Kubernetes

Some of the content of this blog series has been covered in KubeCon US 2019 - a Kubernetes conference 2019. Details of this talk can be found here with recording available here.

Part 1: Reproducibility in Machine Learning - Research and Industry

In Part 1, the objective will be to discuss the importance of reproducibility in machine learning. It will also cover where both research and industry are stand in writing reproducible ML. This blog can be accessed here.

Part 2: Realizing reproducible Machine Learning - with Tensorflow

The focus of Part 2 will be writing reproducible machine learning code. Tensorflow is being used as a machine learning stack for demonstration purposes. This blog can be accessed here.

Part 3: End-to-end reproducible Machine Learning pipelines on Kubernetes

Part 3 is all about realizing end-to-end machine learning pipelines on kubernetes. This blog can be accessed here.