MLOps is a Practice, Not a Tool

Agile organizations have been successful in improving collaboration and reducing waste in software development. They have also learned to automate and streamline their software delivery process. However, many teams are still struggling to leverage the same agile principles through their artificial intelligence (AI) initiatives. Operationalization of machine learning (ML) models is an increasing challenge and a barrier to AI adoption in many companies.

The need for MLOps

For many years now, the software industry is using practices to shorten the development cycles and increase deployment velocity. Continuous integration and continuous delivery help automate the process of building, testing, and deploying high-quality software releases.

In data science, AI experts usually focus on creating the best ML model to solve a business problem. But production-grade ML systems require much more than just some ML code, as shown in figure 1.

Figure 1: ML code lost in an ocean of ML components and activities. Adapted from “Hidden Technical Debt in Machine Learning Systems”, NeurIPS 2015

The ML code (the small box in the middle) represents only a tiny chunk of what is needed to build a business AI solution. That’s why there is a need for a rigorous ML practice that helps teams support and…