MLflow Projects & Model Registry
Stop running ML code in isolation. MLflow Projects make your experiments reproducible on any machine — while the Model Registry gives your models a professional lifecycle: from experiment to staging to production.
Sandip Das
Senior Cloud, DevOps, MLOps & ML Platform Engineer | Heading Cloud, DevOps & MLOps for start-ups | AWS Container Hero | Educator | Mentor | Teaching Cloud, DevOps & Programming in Simple Way
Check out the latest posts
Experiment Tracking with MLflow
Stop losing track of your best model. Learn how MLflow turns chaotic trial-and-error into a reproducible, queryable history of every experiment you've ever run.
Review Week 1 & Build a Simple ML Project
Consolidate everything from Days 1–6 by building a fully versioned, end-to-end Iris classification project with Git, DVC, and a reproducible pipeline.
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.
Data Versioning & DVC Introduction
Why your ML experiments are irreproducible without proper data versioning and how DVC pairs with Git to give every dataset, model, and artifact a permanent, auditable address.
Version Control with Git for ML Projects
Stop losing ML experiments. Learn Git workflows, branching strategies, and .gitignore best practices to make your ML projects reproducible, organized, and production-ready.