There are plenty of good books on machine learning, both theoretical and hands-on. From a
typical machine learning book, you can learn the types of machine learning, major families of
algorithms, how they work, and how to build models from data using those algorithms.
A typical machine learning book is less concerned with the engineering aspects of implementing
machine learning projects. Such questions as data collection, storage, preprocessing, feature
engineering, as well as testing and debugging of models, their deployment to and retirement
from production, runtime and post-production maintenance, are often left outside the scope
of machine learning books.
This book intends to fill that gap.Read more.
The techniques and tools covered in Machine Learning Engineering are most similar to the requirements found in Data Scientist job advertisements.
