My Data Science Workflow: From Raw Data to Production ML

Over the years, I've refined a data science workflow that balances speed with rigor. ## Phase 1: Problem Definition - Define what success looks like - Identify stakeholders and constraints - Set clear metrics ## Phase 2: Data Exploration - Profile data with ydata-profiling - Visualize distributions and relationships - Identify data quality issues ## Phase 3: Baseline - Start with a simple model (linear regression, decision tree) - Establish minimum viable performance - Identify signal strength ## Phase 4: Iteration - Feature engineering cycles - Model selection and hyperparameter tuning - Cross-validation strategy ## Phase 5: Productionization - API development with FastAPI - Docker containerization - CI/CD with GitHub Actions - Monitoring and alerting ## Phase 6: Documentation - Document decisions and trade-offs - Model cards and datasheets - Technical blog posts