Modern Machine Learning Tooling

Photo by Colin Carter on Unsplash

Context

Data Science is evolving at a fast pace and Machine Learning roles are transitioning out from a Data Science hybrid role to more engineering or analysis-oriented roles, often referred to the Type A and B data scientists.

A few evolutions are contributing to these changes:

• An increased embedding of Machine Learning models into production systems, requiring more in-depth technical skills than before
• An increased pace of change in business offering and user behavior, increasing the need for automation.
• An increase in regulatory requirements such as GDPR’s “Right to an Explanation” being put into place, increasing the demand for traceability of data and interpretability of predictions and decision

Shift in tooling

This changing context has caused a change in the tooling used by data scientists. This evolution pushes data scientists towards leveraging the cloud, automation, and interpretability and repeatable processes.

• Could based ML: Cloud infrastructure and Kubernetes (K8S) have changed the way we do Machine Learning. From being able to leverage prebuilt solutions as a Saas application to being able to run a full Machine Learning stack on K8S.
• AutoML and…