MLOps Best Practices

Ideas to Keep in Mind When Developing a ML Pipeline

Photo by Sven Brandsma on Unsplash

By Travis Wolf — Travis Wolf

Challenges arise as the production of machine learning models scale up to an enterprise level. MLOps plays a role in mitigating some of the challenges like handling scalability, automation, reducing dependencies, and streamlining decision making. Simply put, MLOps is like the cousin of DevOps.

It’s a set of practices that unify the process of ML development and operation.

This article serves as a general guide for someone looking to develop their next machine learning pipeline, delivering summaries of topics that will introduce topics of MLOps.

1. Communication and collaboration between roles — “ML products are a team effort”

Production of a successful machine learning lifecycle is a lot like racing in formula one. From the outside, it appears that the driver is the only one responsible for getting the car around the track, but in reality, there are upwards of 80 team members behind the scenes. This is similar to developing an enterprise-level ML product. The data scientist sits in the driver’s seat, directing how the model will be built every step of the…