Why “lifecycle” matters

ML isn’t just training a model.

A model that performs well in a notebook can fail in production because:

• the data distribution changes (drift)
• data quality issues appear
• latency constraints exist
• labels arrive late or are noisy

The lifecycle stages

  flowchart TD
  A[1. Problem Definition] --> B[2. Data Collection]
  B --> C[3. Data Cleaning & Preprocessing]
  C --> D[4. Feature Engineering]
  D --> E[5. Train Model]
  E --> F[6. Evaluate & Validate]
  F --> G[7. Deploy]
  G --> H[8. Monitor & Improve]
  H --> C

1) Problem definition

Decide:

• what is the target?
• what does success mean (metric + threshold)?
• what constraints exist (latency, cost, explainability)?

2) Data collection

Good data beats fancy models.

Typical sources:

• databases (SQL)
• CSV exports
• logs
• APIs

3) Cleaning & preprocessing

Examples:

• missing values
• outliers
• inconsistent categories
• duplicates

4) Feature engineering

Transform raw data into useful signals.

Example: from timestamps create:

• day-of-week
• hour-of-day

5) Training

Fit parameters of your chosen algorithm on training data.

6) Evaluation & validation

Use:

• validation sets and cross-validation
• metrics aligned with the business goal

Watch out for:

• data leakage
• overfitting

7) Deployment

Common forms:

• batch predictions (daily scoring job)
• real-time API
• embedded model (mobile/edge)

8) Monitoring

Monitor:

• input drift (feature distribution changes)
• prediction drift
• performance decay

Key takeaway

ML work is iterative.

You’ll move back and forth between data, features, and training until the model is good enough—and then keep iterating after deployment.

🧪 Try It Yourself

Exercise 1 – Train-Test Split

Exercise 2 – Fit a Linear Model

Exercise 3 – Evaluate with MSE