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

Introduction

In previous chapters, we introduced different databases for different business-use cases. We also introduced the next-generation compute engine Spark for big data analytics. With these tools, we now have all the necessary building blocks for composing any AI data pipeline:

Figure 9.1: A representative flow chart for a typical data pipeline

A typical data pipeline (not limited to AI) looks like the following:

  1. Collect user feedback from a user application.
  2. Store all user feedback and data in a data storage system.
  3. Extract raw user data from the data storage system.
  4. Preprocess raw data into a predefined format so that data science/AI applications can process it.
  5. Cook the processed data into a higher-level view so that business people such as product managers can digest it and make data-informed decisions.

Let's imagine you are working in a data-driven company such as Netflix. Data scientists are building data...