Best Practices for MLOps and the Machine Learning Lifecycle

A successful machine learning (ML) project is about a lot more than just model development and deployment. Machine learning is about the full lifecycle of data. It consists of a complex set of steps and a variety of skills, required to achieve actionable outcomes and deliver business value. The level of complexity involved in the ML lifecycle is part of the reason why good practices and fully integrated tools are in their infancy, even in the present day. Other reasons include a lack of skills, poor scalability of models, and a lack of automation as data scientists often come from several different backgrounds and do not always follow best coding and DevOps practices. Furthermore, data scientists and engineers usually work in silos which results in poor collaboration across the teams.