MLOps Crash Course using MLflow (GenAI - Classic ML)

About this course

Are you struggling to take your machine learning models from a local Jupyter Notebook into a real production environment? The "it works on my machine" problem is real, and it costs companies time, money, and reliability. In this comprehensive crash course, you will learn how to solve this problem using MLflow, the leading open-source MLOps platform used by top AI teams worldwide.

This course is unique because it covers both worlds of modern AI engineering in one unified platform. First, you will dive deep into the Generative AI world. You will learn how to trace LLM prompts and responses, monitor token usage and latency, evaluate your AI agents using custom scorers, manage and version your prompt templates, and set up robust AI Gateways with automatic fallback models. You will even learn how to deploy your agents as live FastAPI endpoints.

Second, you will master Classic Machine Learning. You will use MLflow with Scikit-Learn for automatic experiment tracking, integrate Optuna for powerful hyperparameter tuning, and implement PyTorch checkpointing to protect your deep learning training runs. Throughout the course, you will use the MLflow Model Registry to version, govern, and deploy your models professionally.

By the end of this course, you will have a complete MLOps toolkit and the confidence to manage the entire AI lifecycle, from initial prototype to production deployment. No prior MLOps experience is required. Enroll now and become a production-ready AI Engineer.

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