Introduction
Business and technology professionals have been continuing to face challenges in operationalizing ML for effective development, deployment, and governance. Many of us still view the operationalization process as more of an art than a systemic approach. This results in significant challenges in the scalability and maintenance of the ML models. Why? Because ML initiatives are different from traditional IT product development initiatives.
ML initiatives are very experimental and require skills from many more domains, for example— statistical analysis, data analysis, platform engineering, and application development. Also, there is often a lack of process understanding, communication gap between teams involved, and development and ops teams' unwillingness to engage in each other domains for effective alignment of ML models' development and operationalization.