Recent advances in machine learning have enabled the large-scale automation of a broad spectrum of activities in the machine learning processes. Today, users can automate substantial portions of the ML life cycle, including parts of data pre-processing, feature engineering, model searching, model creation, hyper-parameter optimization, as well as reporting. Further, MLOps methodologies help reduce the cycle time for the deployment of ML models in production while ensuring quality and reliability by incorporating best practices from software engineering.

Date

Thursday, 21^st October 2021