Building the engineering around machine learning.
I am developing a small end-to-end project focused on making a machine learning model reproducible, testable and easier to run. This work is still in progress, so the page separates planned steps from completed implementation.
The problem
A notebook can show that a model works on one dataset. A useful application needs more: consistent preprocessing, a repeatable training run, an interface for predictions, automated checks, versioned results, and a way to notice when things go wrong or the data drifts.
This project brings those tasks into one workflow. It starts with a tabular prediction problem before moving on to more complex systems.
Status: in progress. The steps below are planned milestones. This page will be updated as implementation, tests and deployment evidence are added to the repository.
Tools and methods under development
Model and data
Python, pandas, scikit-learn, validation and reproducible preprocessing.
Serving
FastAPI, request validation, predictable responses and API documentation.
Packaging
Docker, environment configuration, pinned dependencies and local repeatability.
Quality and operations
pytest, CI checks, experiment tracking, logging and performance monitoring.
The plan, step by step
Establish a reproducible baseline
Define the problem and the data contract, build the preprocessing and training pipeline, pick relevant metrics, and document a baseline with clear evaluation.
Package inference as a service
Expose the trained model through FastAPI, validate inputs, return structured errors, and test both expected and invalid requests.
Make the environment repeatable
Containerize the service with Docker, keep configuration out of the code, and add a CI workflow that runs linting and tests on every change.
Track experiments and model versions
Record parameters, metrics and artifacts so any result can be compared and reproduced, rather than depending on undocumented notebook state.
Add operational visibility
Log service behaviour, add health checks, monitor input and prediction distributions, and document limitations and retraining considerations.