DVC Pipelines & Experiment Tracking
Stop running scripts manually. Learn how to define reproducible, dependency-aware ML pipelines using DVC's declarative dvc.yaml, track every run automatically, and share exact pipeline states with your team — no Makefile required.
By Sandip Das · · 12 min read
💡
Hey — It's Sandip Das 👋
As MLOps engineer we will have to increasingly operate ML workflows in production, and understanding DVC pipelines helps us build reproducible, dependency-aware automation with the same discipline used in CI/CD systems.
Learning experiment tracking and pipeline versioning enables reliable collaboration, easier rollbacks, and consistent deployments across teams and environments.
As MLOps engineer we will have to increasingly operate ML workflows in production, and understanding DVC pipelines helps us build reproducible, dependency-aware automation with the same discipline used in CI/CD systems.
Learning experiment tracking and pipeline versioning enables reliable collaboration, easier rollbacks, and consistent deployments across teams and environments.
📚 Key Learnings
- Understand the DVC pipeline execution model
- Write dvc.yaml stages with deps, outs, params
- Use dvc repro for smart stage re-execution
- Inspect pipeline graphs with dvc dag
- Track experiments with dvc exp run
- Build a two-stage preprocess → train pipeline
LearnXops Labs:
🧠 Learn here
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, Community, Python, DVC, DVC Pipelines, Experiment Tracking