Скачать или смотреть MLOps Demystified: MLflow + Kubeflow for Real-World ML Pipelines

Struggling to move your ML models from notebook to production? You’re not alone. In this episode, we decode the entire MLOps lifecycle—from reproducibility nightmares to continuous retraining—and give you hands-on exposure to MLflow and Kubeflow. Learn how to orchestrate, monitor, and scale models with production-grade tooling that’s cloud-ready and open-source. Plus, see it all come together in a real sentiment analysis capstone.

Detailed Article for Developer and Architect:
https://iitmengineer.substack.com/p/m...


MLOps is the secret sauce that turns your machine learning experiments into reliable, scalable production systems. In this video, we break down what MLOps really means and how to implement it effectively using open-source tools like MLflow and Kubeflow.

🔍 What You’ll Learn:

The full ML lifecycle: from problem framing to deployment and monitoring

Key challenges in productionizing ML models

MLflow for experiment tracking, model registry, and reproducibility

Kubeflow for pipeline orchestration, tuning, and serving

A hands-on capstone project: Sentiment Analysis pipeline with both tools

How these skills translate to cloud MLOps platforms like AWS SageMaker, GCP Vertex AI, and Azure ML

Whether you're a data scientist, ML engineer, or DevOps pro, this episode will help you bridge the gap between ML research and production!

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