📊 Build This Week: Track ML Experiments with MLflow in Docker
MLOps Nanoproject PR-ML-01: Start your MLOps journey by building an experiment tracker like the pros use
Namaste Builders,
We’ve reached Week 4 of the RealOps Weekly Nano Projects Series, and this one’s for all of you looking to break into the world of MLOps.
If you’ve ever trained a machine learning model and thought,
"What was that config that worked last time?"
then this week’s project is for you.
We're going to build your very first ML Experiment Tracker using MLflow — the industry standard for tracking, comparing, and managing your model runs.
🧱 Project: Track ML Experiments with MLflow in Docker
Track: MLOps
Level: Intermediate
Time to Build: ~90 minutes
💡 What You'll Build
An MLflow Tracking Server running in Docker
A Python training script that logs metrics and parameters
Multiple experiment runs to compare performance
(Bonus) Logging and saving model artifacts
✅ Build Checklist
Set up MLflow as a standalone tracking server in Docker
Write a basic ML training script (linear regression or classifier)
Log parameters, metrics, and tags for each run
Use the MLflow UI to compare experiment runs
(Bonus) Log the model artifact and prepare it for serving
📦 We’ll provide a starter repo + reference solution on Friday — but you’re encouraged to start building now.
🧠 Why This Matters for MLOps
MLOps starts with discipline — and that begins with tracking what you’re doing.
MLflow is a lightweight but powerful tool used by real ML teams to monitor experiments, compare models, and manage lifecycle.
This week’s project gives you a repeatable system you can reuse in personal and professional ML workflows.
🗓️ Weekly Flow
Monday: Project drops on realops.network + Discord
Friday: Full solution with code, video, and instructions
You: Build during the week or finish over the weekend
🌐 Open to All – XP for Members
These projects are open to everyone.
But as a RealOps Member, you’ll unlock:
🎓 Quizzes + badges on campus.schoolofdevops.com
📈 XP + leaderboard recognition
🔁 Private feedback & Discord roles
Experiment. Track. Compare. Repeat.
It’s how real MLOps is done — and now you’ll know how to do it too.
Let’s go,
— Gourav
Founder, RealOps Builders Network