MLOps with MLflow: Creating & Tracking ML Models

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. With MLflow's tracking capabilities, you can easily log and monitor experiments, keeping track of various model runs, hyperparameters, and performance metrics. In this course, you will dive hands-on into implementing the ML workflow, including data preprocessing and visualization. You will focus on loading, cleaning, and analyzing data for machine learning. You will visualize data with box plots, heatmaps, and other plots and use the Pandas profiling tool to get a comprehensive view of your data. Next, you will dive deeper into MLflow Tracking and explore features that enhance experimentation and model development. You will create MLflow experiments to group runs and manage them effectively. You will compare multiple models and visualize performance using the MLflow user interface (UI), which can aid in model selection for further optimization and deployment. Finally, you will explore the capabilities of MLflow autologging to automatically record experiment metrics and artifacts and streamline the tracking process.