ML Model Development and Tracking: Hands-on Guide

In this course, you will bridge the gap between experimental coding and production-ready machine learning by mastering the "Middle Loop" of the MLOps lifecycle. You will start by refining your model development process, learning to distinguish between standard training and hyperparameter tuning to maximize model performance. To ensure operational efficiency, you will evaluate compute strategies by matching your workloads to the specific strengths of CPUs and GPUs. The core of your experience involves building a robust "Source of Truth" using MLflow to automatically log parameters, track …