MLOps & Tools
PyCaret, Optuna, Weights & Biases, MLflow, DVC, and production deployment strategies.
AutoML with PyCaret
IntermediatePyCaret is an open-source, low-code machine learning library in Python that automates the end-to-end machine learning workflow. It provides a unified interface…
Experiment Tracking with Weights & Biases
IntermediateWeights & Biases (WandB) is a machine learning experiment tracking and visualization platform that helps ML practitioners log, compare, and reproduce…
Hyperparameter Optimization with Optuna
IntermediateOptuna is an open-source hyperparameter optimization framework designed for machine learning. It uses Bayesian optimization with Tree-structured Parzen…
Model Deployment Basics
IntermediateModel deployment is the process of putting a trained machine learning model into a production environment where it can receive real-time or batch data and…
Model Versioning with MLflow and DVC
IntermediateModel versioning is the practice of systematically tracking and managing different versions of machine learning models throughout their lifecycle, similar to…