MLOps in 2021: The pillar for seamless Machine Learning Lifecycle

MLOps is the new terminology defining the operational work needed to push machine learning projects from research mode to production. While Software Engineering involves DevOps for operationalizing Software Applications, MLOps encompass the processes and tools to manage end-to-end Machine Learning lifecycle. Machine Learning defines the models' hypothesis learning relationships among independent(input) variables and predicting target(output) variables. Machine Learning projects involve different roles and responsibilities starting from the Data Engineering team collecting, processing, and transforming data, Data Scientists experimenting with algorithms and datasets, and the MLOps team focusing on moving the trained models to production. Machine Learning Lifecycle represents the complete end-to-end lifecycle of machine learning projects from research mode to production.