End-to-End ML Pipeline
Part of the AI Engineer Roadmap
In short
Training one model in a notebook is step one. This project takes raw data all the way to a deployed, monitored model that retrains itself, which is what ML teams actually run.
A full pipeline from raw data to a monitored, deployed model — the project that proves you can own an ML system, not just a single training script.
Think of it like this
Mangoes from orchard to market
Mangoes are picked, sorted, the bad ones thrown out, packed, trucked and sold. The trader then listens to customers and changes the orchard he buys from. An ML pipeline is the same chain: collect, clean, train, check, ship, then watch and repeat.
Watch it happen
Build plan for the full pipeline
1/6Pull fresh data from a source on a schedule, like a public API or a daily CSV.
The big ideas
What you need to know
Each step is a function
Small, testable steps that pass data along. Easy to rerun one step when it fails.
Champion vs challenger
A new model replaces production only if it beats it on the same data.
Runs on a schedule
Use cron, GitHub Actions on a schedule, Prefect or Airflow, so nobody has to remember to retrain.
Close the loop
Monitoring tells you when the world changed. That's the signal to retrain.
See it in code
Walk through the code
1def run_pipeline(raw_path):2 df = ingest(raw_path)3 validate(df) # raises if schema or null rates look wrong4 model, metrics = train_and_eval(df)5 6 prod = load_prod_metrics()7 if metrics["f1"] <= prod["f1"]:8 print(f"Keeping production: {metrics['f1']:.3f} <= {prod['f1']:.3f}")9 return10 11 version = register(model, metrics)12 deploy(version)13 print(f"Deployed {version} with f1={metrics['f1']:.3f}")1/3Ingest, then validate before spending time on training.
The pipeline's backbone. You write each step function; the shape keeps bad data and worse models out of production.
Save yourself the pain
Common mistakes
Oops
Comparing the new model on fresh data and the old one on last month's.
Do this instead
Score both on the same validation set, or the comparison means nothing.
Oops
One giant notebook that does everything top to bottom.
Do this instead
Split into step functions in .py files so you can test, schedule and rerun them.
Your mission
First milestone: a pipeline that refuses
Quick check
Your new model's F1 is 0.80. Production's is 0.83 on the same data. What should the pipeline do?
Go deeper
- 1Build data ingestion, training, evaluation, deployment and monitoring as connected stages.
- 2Automate at least the training-to-deployment handoff, not just individual steps.
- 3Document the pipeline architecture so someone else could operate it.
More projects at this stop
Deploy a Model as an API
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