MLflow Model Registry for MLOps | Track, Store, Deploy ML Models

🎓 Welcome to the Ultimate MLOps Course 2025 – Your step-by-step guide to deploying machine learning models the right way using MLflow, Docker, Kubernetes, Kubeflow, and CI/CD pipelines.

In this course, you'll get a complete overview of the course structure, what you'll learn, the tools we'll use, and how this course will turn you from a data scientist to an MLOps engineer ready for production environments.

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🧠 MLflow Model Registry for MLOps | Track, Store, Deploy ML Models

Managing machine learning models in production is not just about training good models — it’s about tracking, versioning, and deploying them reliably. That’s where the MLflow Model Registry becomes a powerful tool in your MLOps toolkit. In this video, you’ll learn how to track model experiments, manage versions, and move models through staging to production — all in a structured and scalable way.

Whether you're a data scientist, ML engineer, or MLOps practitioner, mastering the MLflow Model Registry will help you operationalize your models with confidence and control.

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📚 What You'll Learn in This MLOps Course:
✅ MLOps concepts from beginner to advanced
✅ ML experiment tracking & version control using MLflow
✅ CI/CD pipelines using GitHub Actions for ML models
✅ Containerization of ML applications using Docker
✅ Model deployment on Kubernetes with real-world use cases
✅ Introduction to Kubeflow and building Kubeflow Pipelines
✅ Model monitoring, security, logging, and versioning
✅ Scalable production-ready model serving
✅ A full end-to-end deployment case study

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🛠 Technologies You Will Master:
- Python & SkLearn
- Git & GitHub Actions
- MLflow (Experiment Tracking + Model Registry)
- Docker (Containerization)
- Kubernetes (Orchestration)
- Kubeflow (Automation)
- Prometheus, Grafana (Monitoring)

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IMPORTANT LINKS:
🔗 Full Course Playlist: https://www.youtube.com/playlist?list=PL7E7TYb0_SgHM0OLqbRwS0i-q89lsfEq6
📁GitHub Code Repo: https://github.com/edquestofficial/mlops-end-to-end.git
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🎯 Who Is This Course For?
- Data Scientists & ML Engineers
- Backend Developers exploring AI/ML
- Students preparing for AI/ML DevOps roles
- Anyone building production-ready ML systems

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📆 Course Schedule:
This course is divided into 15 structured modules, released every day on this channel. Make sure you subscribe and turn on notifications 🔔 to follow along in real-time.

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Видео MLflow Model Registry for MLOps | Track, Store, Deploy ML Models канала edquest official