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The Artificial Intelligence Infrastructure Workshop

The Artificial Intelligence Infrastructure Workshop

By : Chinmay Arankalle , Gareth Dwyer , Bas Geerdink , Kunal Gera , Kevin Liao , Anand N.S.
4.3 (3)
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The Artificial Intelligence Infrastructure Workshop

The Artificial Intelligence Infrastructure Workshop

4.3 (3)
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)
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Preface
4
4. The Ethics of AI Data Storage

Challenges in Managing Processes in the Real World

We have learned about how to create a task and break it into a multi-stage process. Knowing how to do these two things should be enough to create a functioning data pipeline. But when it comes to managing a data pipeline, there's another important thing to know about: job automation. Imagine that someone updated a source CSV file with the most recent data in the workflow illustrated in Figure 9.01. Someone would need to jump in to manually rerun the entire workflow and deploy a new version of the model.

Automation

To ease the burden of managing hundreds of workflows in a company, we want workflows to be fully automated without any extensive human interaction. If any change happens to one step, it should automatically trigger downstream steps to rerun with the new change. In addition to workflow automation, it'd be nice if we could version each run of the workflow so that we could perform retrospective analysis in the...

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