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.
By Sandip Das · · 11 min read
💡
Hey — It's Sandip Das 👋
Building a complete, reproducible ML project teaches us how to apply software engineering, version control, and automation principles to machine learning workflows.
It provides hands-on experience with Git, DVC, and pipeline orchestration, preparing them to manage ML systems with the same reliability, traceability, and CI/CD practices used in modern cloud-native platforms.
Building a complete, reproducible ML project teaches us how to apply software engineering, version control, and automation principles to machine learning workflows.
It provides hands-on experience with Git, DVC, and pipeline orchestration, preparing them to manage ML systems with the same reliability, traceability, and CI/CD practices used in modern cloud-native platforms.
📚 Key Learnings
- What You Built Over 6 Days
- The 6-Concept Knowledge Map
- What We're Building
- How DVC Tracks Dependencies
- Git vs DVC — What Goes Where
- Build a two-stage preprocess → train pipeline
LearnXops Labs:
🧠 Learn here
What You Built Over 6 Days
Before diving into today's project, let's
Read the full article on learnxops.com.
Topics: 100 Days of MLOps, MLOps, ML, Community, Project