MLflow in Action: End-to-End MLOps Tutorial Series (2025) | EP1: Tracking ML Experiments Made Easy
Автор: Rajan AIML
Загружено: 2025-08-17
Просмотров: 463
Github :: https://github.com/rjn32s/mlflow-learn
🚀 Welcome to the MLflow Mastery Series (2025 Edition) – a complete, hands-on guide to taking your machine learning models from experiments → registry → production → monitoring → GenAI tracing.
In this 7-episode playlist, you’ll learn:
✅ How to track ML experiments with MLflow
✅ Compare and autolog metrics automatically
✅ Register and version models with MLflow Model Registry
✅ Use validation gates before promotion 🚦
✅ Run batch scoring & drift detection
✅ Serve models via REST APIs
✅ Trace and evaluate LLM/GenAI prompts with MLflow 3
By the end of this series, you’ll have a production-ready MLOps pipeline that scales — the same concepts used in top companies.
📌 Episodes:
EP1 → Experiment Tracking
EP2 → Autologging & Metrics
EP3 → Model Registry 🚀
EP4 → Safe Promotion ⚡
EP5 → Batch Scoring & Drift Check
EP6 → REST API Deployment
EP7 → GenAI Tracing + Prompt Eval
💡 Perfect for: Machine Learning Engineers, Data Scientists, and MLOps beginners who want to level up their production ML workflows.
👉 Don’t forget to like, share, and subscribe for more advanced ML, MLOps, Rust, PyCUDA, TensorRT, and deep AI content!
#mlflow #mlops #machinelearning
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