MLflow 3.0 Tutorial: The Ultimate Guide to LLM Tracking & AI Pipelines 🚀 | #mlflow #llmops #ai

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Welcome to the most comprehensive tutorial on the new MLflow 3.0! In this video, we'll dive deep into the latest features that are revolutionizing the MLOps landscape by bringing AI and Large Language Model (LLM) operations into the ecosystem.

You'll learn everything you need to know about LLM tracking and tracing, and I'll walk you through a practical, hands-on implementation. We'll cover how to monitor your LLM's performance, track experiments, and streamline your entire AI pipeline from development to production.

Whether you're a data scientist, machine learning engineer, or just getting started with MLOps, this video will provide you with the practical skills to leverage the full power of MLflow 3.0.

🔗 GitHub Repository with all the code: https://github.com/saikumar0605/mlflow-3.0

In this tutorial, you will learn:

What's new in MLflow 3.0 for AI and LLMs
A step-by-step guide to LLM tracking and tracing
How to set up and manage your experiments effectively
Practical implementation of an end-to-end AI pipeline
Best practices for monitoring and optimizing your models
If you find this video helpful, please give it a thumbs up, subscribe to the channel for more content on MLOps and AI, and let me know in the comments if you have any questions!

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Видео MLflow 3.0 Tutorial: The Ultimate Guide to LLM Tracking & AI Pipelines 🚀 | #mlflow #llmops #ai канала SAI KUMAR REDDY