MLOps vs. DevOps: the similarities and differences Posted on : Nov 20 - 2021

In this post, we explore two terms which are becoming relatively common in professional machine learning applications – MLOps and DevOps

The term MLOps refers to a set of techniques and practises for data scientists to collaborate operations professionals.. MLOps aims to manage deployment of machine learning and deep learning models in large-scale production environments.

The term DevOps comes from the software engineering world and is concerned with developing and operating large-scale software systems. DevOps introduces two concepts: Continuous Integration (CI) and Continuous Delivery (CD). DevOps aims to shorten development cycles, increase deployment velocity and create dependable releases.

Since, an ML system is a software system, DevOps principles also apply to MLOps.

However, there are differences between the two.

Before we explore the differences between MLOps and DevOps, let us look at the overall flow of deploying an ML model intro production.

The overall steps for deploying an ML/DL model in production are:

• Data extraction
• Data analysis
• Data preparation
• Model training
• Model evaluation
• Model serving
• Model monitoring

With this background, here are the differences between MLOps and DevOps

Unlike DevOps, MLOps is much more experimental in nature. Data scientists try different features, parameters, models. In all these iterations, they must manage the code base and create reproducible results. View More