ML Workflow

Introduction

ML Workflow — Repeatable path from collection through deployment and monitoring. This lesson pairs the idea with a minimal Python/sklearn workflow you can extend in a notebook.

Understanding the topic

Concept Repeatable path from collection through deployment and monitoring.

Workflow Load data → preprocess → fit or apply technique → measure on hold-out data.

In practice Start with a small public dataset before jumping to proprietary production data.

• Concept — Repeatable path from collection through deployment and monitoring.
• Workflow — Load data → preprocess → fit or apply technique → measure on hold-out data.
• In practice — Start with a small public dataset before jumping to proprietary production data.

Step-by-step explanation

1. Concept — Repeatable path from collection through deployment and monitoring.
2. Workflow — Load data → preprocess → fit or apply technique → measure on hold-out data.
3. In practice — Start with a small public dataset before jumping to proprietary production data.

Informative example

Python starter:

# ML Workflow — starter sketch
print("Topic: ML Workflow")

Topic: ML Workflow

Execution workflow

Best practices

• Hold out a test set before hyperparameter tuning.
• Scale numeric columns for distance-based models.
• Track multiple metrics — not accuracy alone on skewed labels.

Common mistakes

• Leaking test statistics into preprocessing fit on full data.
• Training on the same rows you report as test performance.
• Chasing complex models before a simple baseline.

Hands-on exercise

Practice:

• Apply ML Workflow on a sample dataset
• Write down one metric that proves the technique helped

Summary

ML Workflow: Repeatable path from collection through deployment and monitoring.