Скачать или смотреть Build an End-to-End ML Pipeline using DVC | Version Control for Data & Models (part 2)

In this video, I will show you how to create a complete Machine Learning pipeline using DVC step-by-step. DVC is one of the most important tools in MLOps because it helps you manage and version control your datasets, models, experiments, and pipelines, making your ML projects reproducible and production-ready.

If you're working on ML projects and want to track your data and model changes just like Git tracks code, then this tutorial is for you ✅
✅ What is DVC and why it's used in MLOps
✅ DVC setup and initialization with Git
✅ Data Versioning (Track datasets easily)
✅ Creating an ML Pipeline using dvc.yaml
✅ Pipeline stages: Preprocessing → Training → Evaluation
✅ Running and reproducing pipelines using dvc repro
✅ Tracking outputs, metrics, and model files
✅ Best practices for structuring an ML project