MLOps, or DevOps for ML, is a burgeoning enterprise area to help Data Science (DS) and IT teams accelerate the ML lifecycle of model development and deployment. Model training, the first step, is central to model development and now widely available on Jupyter Notebooks or with automated training (AutoML). But ML is not the easiest technology to deploy. This results in a majority of ML models never making it to production.
This paper focuses on the final hurdle to successful AI deployment and the last mile of MLOps - ML monitoring.
This paper includes: