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

Model Execution in Streaming Data Applications

In the first part of this chapter, you learned how to export models to the pickle format, to be used in an API. That is a good way to productionize models since the resulting microservices architecture is flexible and robust. However, calling an API across a network might not be the best-performing way to get a forecast. As we learned in Chapter 2, Artificial Intelligence Storage Requirements, latency is always an issue when working with high loads of event data. If you’re processing thousands of events per second and have to execute a machine learning model for each event, your network and pickle file that’s stored on disk might not be able to handle the load. So, in a similar way to how we cache data, we should cache models in memory as close to the data stream as possible. That way, we can reduce or even eliminate the network traffic and disk I/O. This technique is often used in high-velocity stream processing applications...