Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
24
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25
Index

Machine Learning Pipelines

Model training is only a small piece of the machine learning process. Data scientists often spend a significant amount of time cleansing, transforming, and preparing data to get it ready to be consumed by a machine learning model. Since data preparation is such a time-consuming activity, we will present state of the art techniques to facilitate this activity as well as other components that together form a well-designed production machine learning pipeline.

In this chapter, we will cover the following key topics:

  • What exactly is a machine learning pipeline?
  • What are the components of a production-quality machine learning pipeline?
  • What are the best practices when deploying machine learning models?
  • Once a machine learning pipeline is in place, how can we shorten the deployment cycle?