AutoML Pipeline 🚀

An automated machine learning pipeline for model training, evaluation, and deployment with 75% cloud cost reduction through spot instance orchestration.

Features

Feature	Details
Automated HPO	Optuna TPE sampler + Hyperband pruning, 50 trials, parallel
Multi-Model	Random Forest, XGBoost, LightGBM, Logistic Regression
Spot Instances	Dynamic bid pricing, interruption handling, checkpointing
Cost Reduction	~75% savings vs on-demand through spot orchestration
Model Registry	SQLite-backed versioning + auto-promotion to production
Blue/Green Deploy	Health checks, automatic rollback on degradation
REST + WebSocket API	FastAPI with real-time pipeline updates
Premium Dashboard	Glassmorphism dark UI with live Chart.js visualizations

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Project Structure

ML-pipeline/
├── config/
│   └── pipeline_config.yaml      # Master configuration
├── src/
│   ├── pipeline/
│   │   ├── orchestrator.py       # Main pipeline coordinator (async)
│   │   ├── data_ingestion.py     # Load, preprocess, split data
│   │   ├── hyperparameter_tuner.py # Optuna HPO engine
│   │   ├── trainer.py            # Multi-model trainer
│   │   ├── evaluator.py          # Metrics & reporting
│   │   └── model_registry.py     # SQLite registry
│   ├── resources/
│   │   ├── spot_orchestrator.py  # Spot instance lifecycle
│   │   ├── cost_monitor.py       # Real-time cost tracking
│   │   └── resource_allocator.py # Bin-packing scheduler
│   ├── deployment/
│   │   ├── deployer.py           # Blue/green deployment
│   │   └── model_server.py       # FastAPI inference layer
│   └── api/
│       ├── main.py               # FastAPI app (REST + WS)
│       └── websocket_manager.py  # Live broadcast manager
├── dashboard/
│   ├── index.html                # Single-page dashboard
│   ├── style.css                 # Glassmorphism dark theme
│   └── app.js                   # Chart.js + WebSocket client
├── scripts/
│   ├── run_pipeline.py           # CLI entry point
│   └── demo_dataset.py           # Synthetic dataset generator
├── tests/
│   └── test_pipeline.py          # Pytest suite
├── requirements.txt
├── Dockerfile
└── docker-compose.yml

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Quick Start

1. Install Dependencies

cd "c:\Users\Bennerdo\OneDrive\Documents\PROJECTS\ML-pipeline"
pip install -r requirements.txt

2. Run Pipeline (CLI)

# Built-in datasets (iris, wine, breast_cancer, diabetes)
python scripts/run_pipeline.py --dataset iris

# Synthetic classification dataset
python scripts/run_pipeline.py --dataset synthetic_classification

# Skip HPO (faster)
python scripts/run_pipeline.py --dataset iris --no-tune

# Custom CSV file
python scripts/run_pipeline.py --dataset data/mydata.csv --target price --task-type regression

3. Start API Server

set PYTHONPATH=.
uvicorn src.api.main:app --reload --port 8000

Then open the dashboard: dashboard/index.html in your browser, or visit http://localhost:8000/docs for the Swagger API.

4. Docker (Full Stack)

docker compose up --build
# API:       http://localhost:8000
# Dashboard: http://localhost:3000

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API Endpoints

Method	Endpoint	Description
GET	/health	Health check
POST	/pipeline/run	Trigger pipeline run
GET	/pipeline/status	Current run status
GET	/registry/models	List registered models
POST	/registry/promote	Promote model to stage
POST	/models/{name}/predict	Single prediction
POST	/models/{name}/batch_predict	Batch predictions
GET	/cost/summary	Cost savings report
WS	/ws	Real-time updates

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Cost Savings

The pipeline achieves ~75% cost reduction by:

1. Using spot instances for HPO (most expensive stage) at 25-35% of on-demand price
2. Using spot instances for training and evaluation
3. Only using on-demand for ingestion and deployment (reliability-critical)
4. Checkpointing every 60s to survive spot interruptions
5. Fallback to on-demand automatically after max retries

Stage	Spot?	Savings
Data Ingestion	✗	0%
HPO Tuning	✓	70-75%
Model Training	✓	70-75%
Evaluation	✓	70-75%
Deployment	✗	0%
Overall		~75%

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Configuration

Edit config/pipeline_config.yaml to tune:

• hyperparameter_tuning.n_trials — number of Optuna trials
• resources.profiles.*.spot_max_price_ratio — spot bid as fraction of on-demand
• models.enabled — which model families to train
• registry.promotion.min_improvement — score delta required to auto-promote

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Running Tests

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