Deep Learning Pipeline motivation

Question

A Deep Learning Pipeline consists of the following 5 points:

Define and prepare problem
Summarize and understand data
Process and prepare data
Evaluate algorithms
Improve results

Here is the source:

From the book called Deep Learning Pipeline:Building a Deep Learning Model with TensorFlow
What is the exact motivation behind the Deep Learning Pipeline? Why should you use a deep learning pipeline? What's good about a deep learning pipeline? What is the motivation for using a deep learning pipeline?
It's about motivation. What motivates me to create a deep learning pipeline.
Does anyone know/has come across a good book/paper about the Deep Learning Pipelines Motivation?

hH1sG0n3 · Answer

As mentioned in the comments, the 1-5 points apply to any predictive modelling task. I gather your question is probably rather focused on what differentiates deep learning from machine learning and what are the advantages over more traditional approaches. See below some basic points in this respect:

Data size: DL models do particularly well with bigger amounts of structured and especially unstructured data.
Feature extraction: Traditionally, a modeller needs to be aware of the features that are being fed to the ML algorithm based on the domain knowledge and expertise. Conversely, DL algorithms are able to learn and extract features by evaluating abstractions of the data input at various levels.
Generalisation: DL models have better generalization capabilities.

Deep Learning Pipeline motivation

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