MLOps (Machine Learning Operations) is a set of practices that amalgamate software development and operations with machine learning (ML) model development, and it is quickly becoming an integral part of the ML lifecycle. MLOps is defined as a culture of collaboration between data science and DevOps professionals to automate and optimize the delivery of ML systems into production, thereby improving the speed and reliability of those systems. The term MLOps was first coined in 2017 by the Microsoft Azure and Google Cloud Platform teams and has become widely adopted by the ML community.
The goal of MLOps is to create a unified, automated, collaborative process that allows data scientists to deploy ML models to production quickly and efficiently. MLOps provides a way to streamline the iteration and testing cycles that ML models need to go through prior to production deployment. The process helps organizations to maintain control and visibility over the entire ML model development cycle while also ensuring that the models are performing correctly and reliably in production.
The process of MLOps follows the same principles used in DevOps, such as: Continuous Integration (CI) which is the process of committing changes to an application’s code in a single repository that is routinely tested and integrated; Continuous Delivery (CD) which is an automated process that produces and tests code in regular intervals; and Continuous Deployment (CD) which ensures that newly built code models are automatically pushed to production.
MLOps –which is a combination of ML and DevOps—is more than an automated process, as it also includes collaboration, data management, security, monitoring, metrics, and auditing. These practices are essential for the successful deployment of ML models into production.
MLOps helps organizations by automating mundane tasks, improving system availability and reliability, and continuously validating the performance of models in production. Implementing MLOps also allows data scientists to focus on what they do best which is building better models, as the MLOps process takes care of the maintenance and monitoring of those models.
In summary, MLOps is the combination of the best practices of DevOps and ML that enables data scientists to quickly bring ML models into production while continuously monitoring and improving them. MLOps helps bring the best of both worlds — ML and DevOps—into a unified, automated process that significantly reduces the time-to-market for ML models.
