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Keep shoppingMLOps Crash Course using MLflow (GenAI - Classic ML)
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105 students
MLOps Crash Course using MLflow (GenAI - Classic ML)
Master MLOps with MLflow: Track, Evaluate, and Deploy GenAI LLMs and Classic ML Models from Prototype to Production
Created byEmAIoT Academy - Omar Sameh
Last updated 8/2026
Arabic
Arabic [Auto],
What you'll learn
- Set up MLflow tracking server and manage experiments to monitor, evaluate, and debug machine learning models
- Build GenAI systems with LLM tracing, custom scorers, prompt versioning, and AI gateways with fallback models
- Deploy LLM Agents as production-ready FastAPI endpoints using MLflow Agent Servers
- Implement hyperparameter tuning with Optuna and PyTorch checkpointing for deep learning workflows
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Course content
3 sections • 10 lectures • 2h 3m total length
Requirements
- Basic Python programming knowledge is required. No prior MLOps or MLflow experience needed — you will learn everything from scratch
Description
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.
Who this course is for:
- This course is designed for Data Scientists, Machine Learning Engineers, and AI Engineers who want to move their models from local experiments to production-ready systems. It is also ideal for Software Engineers transitioning into ML/AI roles and anyone interested in MLOps. Whether you work with Generative AI (LLMs, Agents) or Classic ML (Scikit-Learn, PyTorch), this course will teach you how to track, evaluate, version, and deploy your models professionally using MLflow.