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

Summary

This chapter introduced some of the most well-known cloud storage solutions, specifically AWS S3. It also covered cloud database solutions for both traditional relational databases and NoSQL databases. Some of the common cloud database solutions are AWS RDS, ElastiCache, DocumentDB, GCP Memorystore, Data Store, and BigTable.

We started by using the AWS CLI in a Terminal to perform common data tasks such as creating a bucket, uploading files, and moving files. Later, we move on to the Python environment, where we used the AWS Python SDK to control AWS resources. At the end of this chapter, we leveraged the practical skills we learned in this chapter and composed an end-to-end pipeline that extracts data from S3, transforms data, and uploads data back to S3. The practical concepts you learned about during these exercises will allow you to build data applications or systems.

In the next chapter, you will continue to build on what you learned in the previous chapters...