Utilize Python and IBM Watson to put real-life use cases into production.


  • Use of popular Python packages for building Machine Learning solutions from scratch.
  • Practice various  IBM Watson Machine Learning tools for Computer Vision and Natural Language Processing applications.
  • Expert-led best practices to put your Machine Learning solutions into the production environment.


This book will take you through the journey of some amazing tools IBM Watson has to offer to leverage your machine learning concepts to solve some real-life use cases that are pertinent to the current industry. 

This book explores the various Machine Learning fundamental concepts and how to use the Python programming language to deal with real-world use cases. It explains how to take your code and deploy it into IBM Cloud leveraging IBM Watson Machine Learning. While doing so, the book also introduces you to several amazing IBM Watson tools such as Watson Assistant, Watson Discovery, and Watson Visual Recognition to ease out various machine learning tasks such as building a chatbot, creating a natural language processing pipeline, or an optical object detection application without a single line of code. It covers Watson Auto AI with which you can apply various machine learning algorithms and pick out the best for your dataset without a single line of code. Finally, you will be able to deploy all of these into IBM Cloud and configure your application to maintain the production-level runtime.

After reading this book, you will find yourself confident to administer any machine learning use case and deploy it into production without any hassle. You will be able to take up a complete end-to-end machine learning project with complete responsibility and deliver the best standards the current industry has to offer.

Towards the end of this book, you will be able to build an end-to-end production-level application and deploy it into Cloud.


  • Review the basics of Machine Learning and learn implementation using Python.
  • Learn deployment using IBM Watson Studio and Watson Machine Learning.
  • Learn how to use Watson Auto AI to automate hyperparameter tuning.
  • Learn Watson Assistant, Watson Visual Recognition, and Watson Discovery.
  • Learn how to implement the various layers of an end-to-end AI application.
  • Learn all the configurations needed for production deployment to Cloud.


This book is for all data professionals, ML enthusiasts, and software developers who are looking for real solutions to be developed. The reader is expected to have a prior knowledge of the web application architecture and basic Python fundamentals.