ML Workflow Overview

The End-to-End Pipeline

graph TD
    A[1. Define Business Question] --> B[2. Collect & Prepare Data]
    B --> C[3. Explore & Understand]
    C --> D[4. Engineer Features]
    D --> E[5. Select & Train Models]
    E --> F[6. Validate & Tune]
    F --> G[7. Interpret & Communicate]
    G --> H[8. Deploy & Monitor]
    H -.->|Iterate| B

Mapping to Assessment

Pipeline Stage	Assessment Section	Key Deliverable
Business Question	Background	Problem statement with hypothesis
Data Preparation	Section A	Documented preprocessing steps
Feature Engineering	Section A	Justified feature choices
Model Selection	Section A	Comparison of 2+ approaches
Validation	Section B	Metrics with confidence intervals
Communication	Section C	Business impact and recommendations

Key Principles

Iterative, not linear — you will revisit earlier stages as you learn more about your data.

Start with the business question — every technical decision should trace back to the problem you are solving.

Compare at least two approaches — the assessment requires comparison. Never present a single model without alternatives.

Document your decisions — the why matters as much as the what. Record why you chose specific preprocessing steps, features, and algorithms.

Assessment Requirement

Your presentation must demonstrate an end-to-end ML workflow from data preparation through to business impact. This site is structured to guide you through each stage.