Thinking-data-science-a-data-science-practitioners-guide

Preface
ix
Chapter 19 (Automated Tools) talks about the automated tools for developing
machine learning applications. The modern tools automate almost all workflows of
model development. These tools build efficient data pipelines, select between
GOFAI (Good Old Fashioned AI—classical algorithms) and ANN technologies,
select the best performing algorithm, ensemble models, design an efficient neural
network, and so on. You just need to ingest the data into such tools and they come up
with the best performing model on your dataset. Not only this, some also spill out the
complete model development project code—a great boon to data scientists. This
chapter gives a thorough coverage of this technology.
The last chapter, Chap. 20 (Data Scientist’s Ultimate Workflow), is the most
important one. It merges all your lessons. In this chapter, I provide you with a
definite path and guidelines on how to develop those highly acclaimed AI applications
and become a Modern Data Scientist.
The entire book at the end will make you a most sought-after data scientist. For
those of you who are currently working as a data scientist, this book will help you
become a modern data scientist. A modern data scientist can handle numeric, text,
and image datasets, is well conversant with GOFAI and ANN/DNN development,
and can use automated tools including MLaaS (Machine Learning as a Service).
So, move on to Chap. 1 to start your journey toward becoming a highly skilled
modern data scientist.
Mumbai, India
Poornachandra Sarang