Day 01: ML Lifecycle & MLflow Introduction | MLOps Tutorial Series ๐
About this lesson
๐ Welcome to Day 01 of the MLOps Tutorial Series! In this video, we kick off our journey into MLOps by exploring the Machine Learning Lifecycle and introducing MLflow, a powerful open-source platform for managing ML experiments and deployments. GitHub Link : https://github.com/jitender-insights/MLOps-Course/tree/main ๐ What You'll Learn in Day 01: โ Overview of the ML Lifecycle โ Key stages of an ML project (Data Collection, Model Training, Deployment, and Monitoring) โ Introduction to MLflow and its Components (Tracking, Projects, Models, Registry) โ How MLflow simplifies ML experiment tracking ๐ก Why This is Important: Understanding the ML lifecycle and tools like MLflow is critical for building robust ML workflows and ensuring seamless collaboration between data scientists and engineers. ๐ฃ Community Links: ๐ท Instagram: https://www.instagram.com/datascilearn/ ๐ฃ YouTube: https://www.youtube.com/@datascilearn ๐บ Telegram: https://t.me/datascilearn ๐ผ LinkedIn: https://www.linkedin.com/company/datascilearn ๐ Stay Connected: Donโt forget to subscribe and follow along for daily lessons on mastering MLOps, complete with practical demos and real-world examples. #MLOps #MLflow #MLLifecycle #MachineLearning #DataScience #MLOpsCourse #ai
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