The Artificial Intelligence Infrastructure Workshop: Build your own highly scalable and robust data storage systems that can support a variety of cutting-edge AI applications

The Artificial Intelligence Infrastructure Workshop: Build your own highly scalable and robust data storage systems that can support a variety of cutting-edge AI applications

English | 2020 | ISBN: 978-1800209848 | 732 Pages | PDF, EPUB | 364 MB


Explore how a data storage system works – from data ingestion to representation
Social networking sites see an average of 350 million uploads daily – a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one.
The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You’ll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you’ll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You’ll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you’ll gain hands-on experience with PyTorch. Finally, you’ll explore ways to run machine learning models in production as part of an AI application.
By the end of the book, you’ll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods.
What you will learn

  • Get to grips with the fundamentals of artificial intelligence
  • Understand the importance of data storage and architecture in AI applications
  • Build data storage and workflow management systems with open source tools
  • Containerize your AI applications with tools such as Docker
  • Discover commonly used data storage solutions and best practices for AI on Amazon Web Services (AWS)
  • Use the AWS CLI and AWS SDK to perform common data tasks