Quick Start

Get up and running with the Rossmann forecasting project in 5 minutes.

New to MLOps Projects?

If you're unfamiliar with Python project setup, check the Detailed Setup Guide for step-by-step instructions.

Installation (3 minutes)

# 1. Install uv package manager
pip install uv

# 2. Clone repository
git clone https://github.com/bradleyboehmke/rossmann-forecasting.git
cd rossmann-forecasting

# 3. Create and activate virtual environment
uv venv
source .venv/bin/activate  # macOS/Linux
# .venv\Scripts\activate  # Windows

# 4. Install dependencies
uv pip install -e .

# 5. (Optional) Set up pre-commit hooks for code quality
uv pip install -e ".[dev]"
pre-commit install

Verify Setup (1 minute)

# Check data files (already included!)
ls -lh data/raw/train.csv data/raw/store.csv

# Run quick test
python -c "import pandas, lightgbm; print('✓ Setup successful!')"

Try a Workflow (1 minute)

Choose one to explore:

DataOps PipelineJupyter NotebooksModel Training

# Run complete data processing pipeline
bash scripts/dataops_workflow.sh

# Launch interactive notebooks
jupyter lab
# Open: notebooks/01-eda-and-cleaning.ipynb

# Train baseline models
python -m src.data.make_dataset

Next Steps

Explore MLOps workflows:

1. 📊 DataOps Workflow - Data validation, processing, versioning
2. 🤖 Model Training - Experiment tracking with MLflow
3. 🚀 Deployment - API and dashboard deployment (Coming Soon)
4. 📈 Monitoring - Data drift and performance tracking (Coming Soon)

Need help? See the Detailed Setup Guide for troubleshooting and advanced configuration.