2.1 Problem Framing¶

Role at Stage 2: Machine Learning¶

Turn product goals into learnable tasks with clear targets, labels, features, and constraints. This part is one capability inside the stage. It should leave behind an artifact, measurements, and a short explanation of failure modes.

Explanation¶

This part has 4 sub-parts because the topic needs that many learning units to feel natural. Some stages have more parts and some have fewer; the structure follows the topic, not a fixed template.

Part Diagram¶

%%{init: {"flowchart": {"htmlLabels": true, "nodeSpacing": 80, "rankSpacing": 110}, "themeVariables": {"fontSize": "18px"}} }%%
flowchart LR
  P["<b>2.1</b><br/>Problem Framing"]
  P --> S1["<b>2.1.1</b><br/>Targets Labels and<br/>Features"]
  P --> S2["<b>2.1.2</b><br/>Prediction Time<br/>Availability"]
  P --> S3["<b>2.1.3</b><br/>Label Noise and<br/>Annotation Quality"]
  P --> S4["<b>2.1.4</b><br/>Baseline Definition"]
  P --> E["<b>Exam</b><br/>Part practice"]

Sub-Parts¶

Sub-part folder	What it explains
2.1.1 Targets Labels and Features	Targets Labels and Features is the working skill inside Problem Framing that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.1.2 Prediction Time Availability	Prediction Time Availability is the working skill inside Problem Framing that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.1.3 Label Noise and Annotation Quality	Label Noise and Annotation Quality is the working skill inside Problem Framing that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.1.4 Baseline Definition	Baseline Definition is the working skill inside Problem Framing that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.

What a Person Who Masters This Part Can Do¶

Explain how Problem Framing supports an ml baseline report comparing simple and stronger models with metrics, error slices, and a model card..
Build and inspect this artifact: Create a task framing document.
Measure progress with: Report target definition, label source, feature availability, and prediction-time constraints.
Debug at least one failure mode before moving to the next part.

Build and Measure¶

Build: Create a task framing document.

Measure: Report target definition, label source, feature availability, and prediction-time constraints.

Tests¶

Take one 30-question exam after studying this part. It opens in a new browser tab so the study page stays available.

Open Part Exam

Back to Stage¶

Return to Stage 2: Machine Learning.