Pragmatic AI An Introduction to Cloud-Based Machine Learning

Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning

Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results?even if you don?t have a strong background in math or data science.

Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you?ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.

Building on these fundamentals, you?ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you?re a business professional, decision-maker, student, or programmer, Gift?s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment.

• Get and configure all the tools you?ll need
• Quickly review all the Python you need to start building machine learning applications
• Master the AI and ML toolchain and project lifecycle
• Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn
• Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems
• Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services
• Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more
• Work with Microsoft Azure AI APIs
• Walk through building six real-world AI applications, from start to finish

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Foreword

Preface

Acknowledgments

About the Author

Part I: Introduction to Pragmatic AI

Chapter 1: Introduction to Pragmatic AI

Chapter 2: AI and ML Toolchain

Chapter 3: Spartan AI Lifecycle

Part II: AI in the Cloud

Chapter 4: Cloud AI Development with Google Cloud Platform

Chapter 5: Cloud Ai Development with Amazon Web Services

Part III: Creating Practical AI Applications from Scratch

Chapter 6: Predicting Social-Media Influence in the NBA

Chapter 7: Creating an Intelligent Slackbot on AWS

Chapter 8: Finding Project Management Insights from a Github Organization

Chapter 9: Dynamically Optimizing EC2 Instances on AWS

Chapter 10: Real Estate

Chapter 11: Production AI for User Generated Content (UGC)

Appendix A: AI Accelerators

Appendix B: Deciding on Cluster Size

Index

Noah Gift is lecturer and consultant at UC Davis Graduate School of Management in the MSBA program. Professionally, Noah has approximately 20 years’ experience programming in Python and is a member of the Python Software Foundation. He has worked for a variety of companies in roles ranging from CTO, general manager, consulting CTO, and cloud architect. Currently, he is consulting start-ups and other companies on machine learning and cloud architecture, and is doing CTO-level consulting via Noah Gift Consulting. He has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps. He is also a certified AWS Solutions Architect. Noah has an MBA from University of California, Davis; an MS in computer information systems from California State University, Los Angeles; and a BS in nutritional science from Cal Poly, San Luis Obispo. You can find more about Noah by following him on Github (https://github.com/noahgift/), visiting http://noahgift.com, or connecting with him on https://www.linkedin.com/in/noahgift/.

• Illuminates the latest solutions from Google, Amazon, and Microsoft, plus powerful techniques based on Python and R
• Real-world examples and workflows show how machine learning, AI, and cloud computing come together in real business solutions
• Covers every step from deployment to production, focusing on cloud environments that maximise scalability and simplify deployment
• Addresses coding, libraries, statistical techniques, modeling for fast prototyping, and more