Install MLflow and Start Tracking
Spin up the tracking server and point your code at it.
Install MLflow and Start Tracking is a free MLOps Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Track at All
You run dozens of experiments and forget which settings gave the best score. MLflow records every run so no result is ever lost again.
Install It
MLflow is just a Python package. One pip install gives you the library, a tracking API, and a built-in web UI to browse runs.
pip install mlflowThe Tracking Server
The tracking server is where runs are stored. Start one locally and it serves an API plus a dashboard on port 5000.
mlflow server --host 127.0.0.1 --port 5000Point Your Code At It
Tell MLflow where the server lives with set_tracking_uri. After this, every run you log flows to that server.
import mlflow
mlflow.set_tracking_uri("http://127.0.0.1:5000")What a Run Is
A run is one execution of your training code. It bundles the parameters you chose, the metrics you got, and any files you saved.
Start a Run
Wrap your training in start_run. MLflow opens a run, gives it a unique ID, and closes it cleanly when the block ends.
with mlflow.start_run():
train_model()Experiments Group Runs
An experiment is a named folder for related runs. Set one so all your churn-model attempts live together instead of scattered.
mlflow.set_experiment("churn-model")Where Runs Are Stored
Behind the scenes MLflow writes run metadata to a backend store, like a SQLite or Postgres database, so it survives restarts.
Local Files Mode
No server yet? MLflow falls back to a local mlruns folder. Great for quick tests, but a shared server is better for teams.
Open the UI
Browse everything you logged in the MLflow UI. Just visit the server URL in a browser to see your runs as a sortable table.
Tracking Is Step One
Tracking is the foundation of MLOps. Once every run is captured, you can compare, reproduce, and promote models with confidence.
Quick Check
Let us check how you connect your code to MLflow.
Recap
You installed MLflow, started a tracking server, pointed your code at it, and opened a run. Every experiment now has a home. ✅
Frequently asked questions
Is the “Install MLflow and Start Tracking” lesson free?
Yes — the full text of “Install MLflow and Start Tracking” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Install MLflow and Start Tracking”?
Spin up the tracking server and point your code at it. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Install MLflow and Start Tracking” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Install MLflow and Start Tracking
- Log Params, Metrics, and Artifacts
- Compare Runs in the MLflow UI
- Autolog with One Line of Code