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

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.