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Introduction
Machine Learning Operations or MLOps for short is the process of putting machine learning models into production, then maintaining and monitoring them on a continuous basis is the core objective of MLOps. MLOps is a team sport combining skills and expertise from data scientists, data engineers, machine learning engineers, and DevOps engineers. The primary benefits of MLOps are efficiency, scalability, and reproducibility.
MLOps includes everything from the data flow to the deployment of machine learning models. In some situations, MLOps implementation is just used for model deployment, but you can also find more mature enterprises that have implemented MLOps across a variety of ML Lifecycle development domains, such as exploratory data analysis (EDA), data preprocessing, model training, etc.
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When it comes to tools and technologies that data scientists and ML Engineers use for MLOps, Python is the number one choice. It has a growing ecosystem of both the open-source and enterprise MLOps tools. This article lists down some of the really useful MLOps technologies…
