Mlops Spanning Whole Machine Learning Life Cycle: Paper Summary

This paper provides an extensive examination of MLOps, an emerging discipline that focuses on automating the entire machine learning lifecycle. The survey covers a broad range of topics, including MLOps pipelines, challenges, and best practices. It delves into the various phases of the machine learning process, starting from model requirements analysis, data collection, data preparation, feature engineering, model training, evaluation, system deployment, and model monitoring. Additionally, it discusses important considerations such as business value, quality, human value, and ethics throughout the entire lifecycle.