MLOps: Maturity level 0 to 1

Setting up an efficient ML pipeline is crucial for operationalizing services and products. The ability to automate the necessary parts of the pipeline allows ML Engineers to reliably flow data from source(s) to destination(s). Intermediate steps in the pipeline allow for transformation, versioning, and storing for later consumption of data. ML training experiments can be more easily integrated into a continuous process, instead of relying on manual execution of partial portions of the ML Pipeline.

We will walk through a Sports Analytics project that uses real-world data to showcase the difference between an ML Pipeline at maturity stage 0 and maturity stage 1. We will see how to integrate and leverage SuperAnnotate as the Labeling/QAing/Feature Extraction tool in our ML Workflow. The data extracted with the help of SuperAnnotate will serve as the input for training a series of models which in turn are used to implement the real-world solution to our Sports Analytics problem.