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

Apache Spark

Apache Spark is a unified engine that is based on a parallel cluster-based computing model extending the Hadoop MapReduce model. It developed as an open-source project from the research of a Ph.D. student at Berkeley. Apache Spark can handle various parallel and different workloads to process big data.

Spark's foundation layers are low-level granular APIs and structured APIs. The low-level APIs work on RDDs and distributed variables, while the structured APIs work on datasets, DataFrames, and support querying using SQL. Spark's streaming functionalities, unified advanced analytics, and the ecosystem of numerous libraries are built on the foundations of low-level and structured APIs:

Figure 7.1: Spark architecture

Apache Spark emerged as an attempt to bridge and close some of the gaps in Hadoop/MapReduce. While MapReduce has had its early adoption and traction as a general batch processing engine in companies such as Facebook and Google...