ML Model Development and Tracking: Hands-on Guide

Overview

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 metrics, and manage model versions with professional precision. You will move beyond manual tracking by implementing a centralized dashboard that allows for seamless comparison of hundreds of experimental runs. To maintain organizational integrity, you will master the MLflow Model Registry to handle artifact versioning and transitions from staging to production. The course culminates in a hands-on capstone where you will launch a live MLflow server and generate synthetic datasets to simulate a real-world insurance claim review system. By the end, you will have established a fully reproducible training environment, ensuring your AI solutions are organized, searchable, and ready for high-scale deployment.

Syllabus

Model Development

Focus on the core foundations of building high-performance machine learning models. You will explore the technical nuances of model training and hyperparameter tuning to maximize accuracy while understanding the hardware requirements of the CPU vs. GPU landscape. This module bridges the gap between theoretical algorithms and the physical compute power needed to run them efficiently.

Experiment Tracking

Transition from manual tracking to professional MLOps practices by mastering MLflow. This module provides a deep dive into setting up tracking servers, logging parameters, and managing model artifacts and versioning. Through hands-on labs, you will learn how to maintain a searchable, reproducible record of every experiment you run, ensuring no breakthrough is ever lost.

Automating Insurance Claim Reviews with MLflow and BentoML

Apply your development and tracking skills to a real-world business case: Automating Insurance Claim Reviews. You will start by generating synthetic datasets and configuring a dedicated MLflow server to manage the project lifecycle. This phase focuses on establishing a robust end-to-end pipeline that moves your model from a local script to a professional, tracked experiment environment.