Скачать или смотреть MLflow + Transformers: Track and Fine-Tune Models Like a Pro

In this tutorial, you’ll learn how to fine-tune transformer models (like DistilBERT, RoBERTa, etc.) while tracking experiments seamlessly using MLflow. Whether you're working on NLP tasks or custom model architectures, this video shows you how to combine the power of transformers with the flexibility of MLflow for end-to-end machine learning workflows.

✅ What You’ll Learn:

Setting up your transformer model for fine-tuning
Integrating MLflow for logging metrics, parameters & artifacts
Organizing experiments and comparing runs
Deploy-ready tracking for your fine-tuned models

🚀 This is a must-watch if you’re working with Hugging Face, PyTorch, or TensorFlow and want to add professional tracking and reproducibility to your workflow.

00:00 - Introduction
01:00 - Preparing Transformer Dataset
05:00 - Fine-Tuning Transformer with Hugging Face
11:00 - Integrating MLflow for Experiment Tracking
13:00 - Logging Parameters, Metrics, and Artifacts
14:00 - Visualizing and Comparing MLflow Runs

🔗 Resources Mentioned:

MLflow Docs: https://mlflow.org
Hugging Face Transformers: https://huggingface.co/transformers

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