The Emerging Field of Machine Learning Operations (MLOps)

Akanksha

The machine learning lifecycle, which includes various stages such as model training, deployment, management, and monitoring, is continuously evolving. So amidst this, how cool will it be to have something that smooths over the communication gap between data scientists and information technology (IT) professionals, while allowing organizations to scale their production capacity to the point of generating significant business value and delivering results.

This is where MLOps, i.e., Machine Learning Operations, comes into the picture.

What is MLOps?

MLOps is the discipline that combines technology and practices providing a scalable and controlled means to deploy machine learning models in production environments. It aims to capture, learn, and expand on previously applied practices while also exposing the system to new challenges. It comes in handy when it comes to supporting the continuous integration, development, and delivery of AI/ML models into production on a large scale.

The objective of MLOps is to deploy and maintain machine learning models stably and efficiently. It is a field that unites the models of Machine Learning, DevOps, and Data Engineering.

MLOps have found a vast area of use. Some of them have the ability to speed up experimenting while developing models and track the version of the model. It utilizes AI to benefit its design while reducing the time in model perfection end to end. All this is done while keeping the Machine Learning lifecycle in check. MLOps also enable permission control. It improves traceability by automatically tracking all changes made to models.

Now the question arises, how can MLOps be used in real-time?

MLOps allows or rather facilitates the data scientists to do their work without worrying about the aspects of model execution. MLOps does what it does best; it takes the operational factor of the model into the picture. This helps us gain insight from the available and past data and turn them into usable data that benefits the model.

Your model may provide good results, but if your MLOps area is not substantial, your users may not be happy with the results. A Forrester Research survey titled “Operationalize Machine Learning” shows that 97% of enterprises reported that having mature processes to deploy and operationalize machine learning fast and scalably is essential to success. The time has come to embrace the potential of end to end and effective machine learning in specific establishments.

Sources/Image sources :

  1. https://techcrunch.com/sponsor/microsoftazure/why-firms-are-welcoming-mlops-into-the-fold-of-software-development/
  2. https://en.m.wikipedia.org/wiki/MLOps#:~:text=MLOps%20(a%20compound%20of%20%E2%80%9Cmachine,(or%20deep%20learning)%20lifecycle.
  3. https://ml-ops.org/content/mlops-principles
  4. https://blog.basis-ai.com/what-is-mlops-and-why-is-it-so-critical-for-enterprise-ai

About the Author: Akanksha Bose is a Second Year Electronics and Telecommunications Engineering student at RAIT.