MLOps-CI-Pipeline

An end-to-end MLOps pipeline demonstrating automated retraining, evaluation-based promotion, versioned deployment, and data drift monitoring, designed for a bachelor-level engineering project.

Requirements

Python 3.12.x is required. Other versions are not officially supported.

Setup

1. Create virtual environment

macOS / Linux

python3.12 -m venv .venv
source .venv/bin/activate

Windows

py -3.12 -m venv .venv
.\.venv\Scripts\Activate

2. Install project in editable mode

pip install --upgrade pip
pip install -e .

CLI commands

All commands become available after pip install -e ..

Run pipeline

Runs the full pipeline: versioning → validation → split → preprocessing → training → evaluation → promotion. All data preparation is handled automatically.

# Tabular classification (CSV datasets)
run-pipeline --config src/config/pipeline_tabular.yaml

# Image classification — CNN with PyTorch (JPG/PNG)
run-pipeline --config src/config/pipeline_image.yaml

# Raw DNG images through ISP pipeline → CNN
run-pipeline --config src/config/pipeline_raw_cnn.yaml

First run with a new dataset: if dataset.yaml is missing, the pipeline will prompt you interactively for target column and task type. This only happens once.

Roll back a model

Rolls back the Production model to a previous registry version. Omit --version to roll back to the most recent Staging version. --yes skips the confirmation prompt.

rollback-model --config src/config/pipeline_tabular.yaml
rollback-model --config src/config/pipeline_tabular.yaml --version 2 --reason "accuracy regression" --yes

Start the prediction API

Starts the FastAPI prediction service. Host and port are read from src/config/deployment.yaml. Requires at least one model promoted to Production via run-pipeline first.

run-api

The API exposes:

• GET /health — liveness check
• POST /predict — single-sample prediction (JSON body)

Run tests

python -m pytest tests/ -v --tb=short

Docker

The prediction service can be deployed as a Docker container. It loads the current Production model from the MLflow registry at startup.

# Prerequisites: a Production model must exist (run pipeline and approve first)
run-pipeline --config src/config/pipeline_tabular.yaml

# Copy environment template
cp .env.example .env

# Build and start
docker compose -f docker/docker-compose.yml up --build

# Verify
curl http://localhost:8000/health

# Stop
docker compose -f docker/docker-compose.yml down

# Rebuild after config/code changes without losing volumes
docker compose -f docker/docker-compose.yml up --build --force-recreate

MLflow tracking UI

Browse experiments, runs, metrics, and registered models:

mlflow ui --backend-store-uri mlruns

Open http://localhost:5000 in a browser.

Pipeline stages

Stage	Description
preprocessing	Selects features, normalises, writes preprocessed/ (CSV or NPZ)
training	Trains the model defined in training_*.yaml; saves artifact to registry
evaluation	Computes metrics (accuracy, F1, etc.) and drift tests against reference data
promotion	Promotes model to Production if promotion rules pass; requests approval if configured

Data flow

Tabular datasets:

data/raw/<dataset>/data.csv
        ↓  versioning
data/processed/<dataset>/<version_id>/data.csv  +  train/  val/  test/
        ↓  preprocessing
data/processed/<dataset>/<version_id>/preprocessed/  train.csv  val.csv  test.csv

Image datasets (JPG/PNG):

data/raw/<dataset>/images/<class>/...
        ↓  versioning + stratified split
data/processed/<dataset>/<version_id>/{train,val,test}/images/<class>/...
        ↓  preprocessing (resize, normalise, flatten)
data/processed/<dataset>/<version_id>/preprocessed/  train.npz  val.npz  test.npz

Raw DNG datasets (ISP pipeline):

data/raw/<dataset>/images/<class>/*.DNG
        ↓  versioning + stratified split
data/processed/<dataset>/<version_id>/{train,val,test}/images/<class>/*.DNG
        ↓  ISP pipeline (black level → demosaicing → white balance →
           colour correction → denoising → sharpening → gamma)
        ↓  resize, normalise, flatten
data/processed/<dataset>/<version_id>/preprocessed/  train.npz  val.npz  test.npz

Adding datasets

See data/raw/README.md for instructions on how to add new datasets.