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

Streaming Data

The requirements for a streaming data layer are different from a batch-oriented data lake. Firstly, the time dimension plays a crucial role. Any event data that enters the message bus as a stream must be timestamped. Secondly, performance and latency are more important since it must be certain that data can be processed in due time. Thirdly, the way that analytics and machine learning are applied differs; while the data is being streamed in, the system must analyze it in near-real-time. In general, streaming data software relies more on computing power than storage space; processing speed, low latency, and high throughput are key. Nevertheless, the storage requirements that are in place for a streaming data system are worth considering and are a bit different from "static" batch-driven applications.

Security

A typical streaming datastore is separated into topics. A topic is named as such in the popular streaming data store Kafka. These can be considered...