liveBook · Manning

End-to-end model

As explained in Machine Learning System Design

A model architecture that maps inputs directly to final outputs without explicit intermediate representations or manually designed processing stages.

An end-to-end model maps inputs directly to final outputs through a single learned system, avoiding explicit intermediate representations and manually designed processing stages. section 3.3

This architecture can:

  • Capture complex relationships without the assumptions and limits built into manually designed pipelines. section 3.3
  • Require less domain knowledge and reduce error accumulation between sequential processing steps. section 3.3
  • Still depend on external databases in applications where the model does not contain the required knowledge. section 3.3

Text-to-speech

A traditional TTS pipeline converts text into phonemes, stress marks, and intonation patterns before synthesizing speech. An end-to-end TTS model instead maps text directly to an audio waveform with one neural network. section 3.3

End-to-end success does not eliminate system tradeoffs: large language models can answer directly with zero-shot performance, but they are computationally expensive and can hallucinate. section 3.3

How Machine Learning System Design covers it

How deep it goes
Compares architectural rationale and a TTS example, but stops short of training procedures, implementation details, and model internals.
Examples
Uses text-to-speech conversion and direct text-to-audio waveform synthesis as the central architectural comparison.
What you can do after
Evaluate when direct learned mappings offer advantages over decomposed pipelines and recognize their knowledge, cost, and reliability limits.
How it connects
Connects deep learning, machine learning pipelines, text-to-speech, large language models, zero-shot learning, and hallucination.
Cover of Machine Learning System Design
liveBookmanning.com
Read Machine Learning System Design on liveBook Get the book at manning.com

Also explained in

Compare how 2 books explain end-to-end model →

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.