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If each individual component of an ML pipeline is at or near human-level performance but the overall pipeline falls far short of human-level performance, the pipeline is usually flawed and needs redesign.

Flawed Pipeline Despite Human-Level Components

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

A pipeline can fail because a later component is doing as well as it can given its inputs, but those inputs do not contain enough information. Pipeline redesign should ask what other information a skilled human would need.

Missing Information in Pipeline Inputs

What is the only valid conclusion when every ML pipeline component is at human-level but the overall pipeline is significantly below human-level?

If every individual component of an ML pipeline achieves human-level performance, the overall pipeline is guaranteed to also achieve human-level performance.

When every component performs at human-level yet the overall pipeline falls short, the pipeline is usually _____ and needs to be redesigned.

Match each pipeline diagnosis concept to its correct description in Ng's framework.

Order the diagnostic steps for determining whether an ML pipeline with human-level components is structurally flawed.

In the self-driving car example, humans given only camera images plan far better paths than the assembled ML pipeline. What does this reveal?

Error analysis on an underperforming ML pipeline can help reveal whether the pipeline structure itself needs to be redesigned.

When assessing a pipeline component against human-level performance, the human baseline must be given the _____ as that component.

Match each pipeline scenario to the correct diagnosis it warrants according to Ng.

Order the reasoning steps Ng uses to conclude that an ML pipeline must be redesigned when all components are already at human-level.

Question:
Discuss the implications of an ML pipeline where every individual component achieves human-level performance, yet the overall system falls significantly short of human-level performance. Explain what this scenario reveals about the system and what the necessary course of action is.

Sample answer:
When every individual component of an ML pipeline achieves human-level performance but the overall system still underperforms compared to a human, the only possible conclusion is that the ML pipeline itself is structurally flawed. This indicates that the current architecture is insufficient to achieve the overall goal, even with perfect components. The necessary course of action is to redesign the pipeline.

Key points:
- The overall ML pipeline is structurally flawed.
- The pipeline must be redesigned.
- Individual component success does not guarantee overall system success.

Rubric:
A strong response should correctly identify that the pipeline is structurally flawed and requires redesigning. It should clearly state that individual component success does not guarantee overall system success.

Analyzing Underperforming Pipelines with High-Performing Components

Case context:
You are leading an ML team building a self-driving car. Through rigorous testing, you verify that your object detection, lane tracking, and path planning components all perform at human-level when evaluated individually. However, the overall self-driving car system plans significantly worse paths than a human given the same camera images.

Question:
Based on Andrew Ng's framework, diagnose the fundamental problem with your self-driving car system and state the necessary decision your team must make.

Sample answer:
The fundamental problem is that the ML pipeline itself is flawed. Because the system underperforms a human despite having human-level components, the overall architecture is insufficient. The team's necessary decision is to redesign the ML pipeline entirely.

Key points:
- Diagnose the ML pipeline as flawed.
- Decide to redesign the pipeline architecture.

Rubric:
The answer must correctly conclude that the overall pipeline is flawed and state that the team needs to redesign it.

Diagnosing a Self-Driving Car Pipeline

Question:
According to Andrew Ng, what is the required action when an ML system falls far short of human-level performance despite every individual component operating at human-level?

Sample answer:
The required action is to redesign the ML pipeline, as the current pipeline is flawed.

Key points:
- The pipeline is flawed.
- The pipeline needs to be redesigned.

Rubric:
Award full credit if the learner states that the pipeline must be redesigned.

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