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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

Getting Started with PyTorch

PyTorch is one of the most popular open-source deep learning libraries in the world right now. It's known for its fast iteration, model ideation, and prototyping. As a result, many AI researchers or engineers implement their state-of-the-art deep learning models through the PyTorch library or its ecosystem. PyTorch has a large machine learning community and its community continues to grow and mature. Another popular deep learning framework is TensorFlow. TensorFlow gained its popularity a little earlier than PyTorch. Let's compare the differences in their core features:

Figure 11.10: PyTorch versus TensorFlow

Generally speaking, PyTorch is more development-friendly and TensorFlow is more deployment-friendly. Both are very powerful deep learning frameworks. If you want a better development and research experience, PyTorch is a better fit for you. On the other hand, if you want to deploy your models to production, then TensorFlow...

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