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

Data Processing Techniques

In Chapter 2, Artificial Intelligence Storage Requirements, we discussed the layers of a modern data lake and the requirements and possible data storage options for each layer. It became clear that data has to be sent to different data stores to maximize the abilities of AI: building a historical overview and a high-performing queryable source. This means that some work needs to be done with the data before it's suitable for a machine learning model. These data transfers usually happen as ETL steps in a data pipeline. We'll dive into the specifics and possibilities of batch processing in the following paragraphs.

Transactions

In databases, a transaction is a fixed set of instructions that either fail or succeed. Transactions are very useful for data processing since they are reliable and produce no undesirable outcomes. Use them when certain steps are related, or have to be done in a certain order. If a transaction is composed of a hundred...