How to use MLOps for an effective AI strategy

87% of machine learning projects fail to make it into production. Deploying ML models in business use cases involves working around several data and engineering bottlenecks that impede the implementation process. In fact, ML teams spend a quarter of their time trying to develop the infrastructure needed to deploy ML.

The Machine Learning Lifecycle
(Adapted from Microsoft’s — Data Science Lifecycle)

What is MLOps

MLOps — a set of best practices aimed at automating the ML lifecycle — brings together the ML system development and ML system operations. An amalgamation of DevOps, machine learning, and data engineering, MLOps simplifies machine learning deployment issues in diverse business scenarios by establishing ML as an engineering discipline.

MLOps framework for success

Since MLOps is a nascent field, it can be difficult to get a grasp of what it entails and its requirements. One of the foremost challenges in implementing MLOps is the difficulty in superimposing DevOps practices on ML pipelines. This is primarily due to the fundamental difference: DevOps deals with code, whereas ML is code and data. And when it comes to data, unpredictability is always a major concern.

Exploring the ML pipeline (CI/CD/CT)

Data teams need to look at MLOps simply as a code artefact that stands independent of individual data instances. This is why, breaking it up into two distinct pipelines (training pipeline and serving pipeline) can help ensure a safe run environment for batch files as well as an effective test cycle.

A schematic representation of the complete model preparation process

Building the right team

The MLOps team should ideally include members from the operations, IT and data science division. An enterprise leader with experience in operationalizing machine learning should lead this team.

Advantages of MLOps & the way forward

The foremost benefit of leveraging MLOps is the rapid, innovative ML lifecycle management. MLOps solutions make it easier for data teams to collaborate with IT engineers and increases the speed of model development. Moreover, the provision to monitor, validate, and manage systems for machine learning models expedites the deployment process.

About the author

Anwar is a business transformation manager at Sigmoid. For nearly a decade, he has led development and deployment of scalable AI solutions for clients in various industry domains helping them advance their analytics journey.

Innovative Data Solutions at Scale — Open Source | Cloud | ML | AI