ML Practice: End-to-End Project Workflow
266 words
1 min read
Visual companion
Python
Type and operator map
Python Week 1: the first filter for runtime behavior
View
Revision summary
What this note is really saying
Short form
# ML Practice: End-to-End Project Workflow ## 🎯 Learning Objectives - Execute a complete ML project from problem definition to deployment - Apply data cleaning, EDA, and feature engineering to real datasets - Select, tune, and evaluate models systematically - Understand deployment considerations ## 📖 Core Content...
ML Practice: End-to-End Project Workflow
🎯 Learning Objectives
- Execute a complete ML project from problem definition to deployment
- Apply data cleaning, EDA, and feature engineering to real datasets
- Select, tune, and evaluate models systematically
- Understand deployment considerations
📖 Core Content
1.1 The ML Project Lifecycle
(Diagram)
1.2 Problem Definition Framework
| Component | Questions to Answer |
|---|---|
| Business Goal | What business metric do we improve? |
| ML Task | Classification, regression, clustering, recommendation? |
| Success Metrics | Precision, recall, RMSE, business KPIs? |
| Constraints | Latency, interpretability, data privacy, budget? |
| Baseline | What's the current (non-ML) approach? |
1.3 Project Checklist
- Define: Frame the ML problem, set success criteria, identify stakeholders
- Explore: Profile data, check distributions, identify issues
- Clean: Handle missing values, outliers, inconsistencies
- Engineer: Create features, encode categories, scale
- Model: Start simple (linear), iterate to complex
- Tune: Cross-validate, hyperparameter search
- Evaluate: Test set, business metrics, error analysis
- Deploy: API, monitoring, CI/CD pipeline
📝 Practice Questions
Q1: Why start with a simple model (linear regression, logistic regression)?
- Baseline: Know if complex models are worth it
- Debugging: Simpler to verify data pipeline
- Fast iteration: Quick feedback on data quality
- Interpretability: Stakeholders understand linear models
- Production: Simple model may be good enough (cheaper, faster) Q2: What's the difference between a business metric and an ML metric?
Business metric: revenue increased, costs reduced, customer retention improved. ML metric: accuracy, F1, RMSE. They must be aligned — improving ML metrics should improve business metrics. Sometimes they conflict (higher accuracy doesn't always mean higher revenue). Join Discord NextData Cleaning