Book Image

The Artificial Intelligence Infrastructure Workshop

By : Chinmay Arankalle, Gareth Dwyer, Bas Geerdink, Kunal Gera, Kevin Liao, Anand N.S.
Book Image

The Artificial Intelligence Infrastructure Workshop

By: Chinmay Arankalle, Gareth Dwyer, Bas Geerdink, Kunal Gera, Kevin Liao, Anand N.S.

Overview of this book

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.
Table of Contents (14 chapters)
Preface
4
4. The Ethics of AI Data Storage

2. Artificial Intelligence Storage Requirements

Activity 2.01: Requirements Engineering for a Data-Driven Application

Solution

  1. Taxi data (GPS locations, current rides), HR system data (drivers), map data, phone calls, and email records with customer interaction, website, and app input (queries for rides, page visits).

    The layers for the solution are as follows:

    Figure 2.16: Layers in a data-driven application

  2. There are daily updates from source systems: raw -> historical -> analytics layer.

    There is a streaming data pipeline for events from taxis.

  3. The minimum set of metadata to capture is the source, owner, date, type, and the transformations that have been applied to the data records. This metadata can be used for auditing, security and consent management, and lineage reports.
  4. The solution will probably receive an AIC rating of 323 or higher since it contains sensitive and private data (personnel records, GPS locations, and so on). Therefore, security...