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

Understanding Various Spark Actions

Spark actions trigger specified transformations. Transformations create RDDs from another RDD. Actions are the operations that are performed on RDDs to give non-RDD values.

Popular actions include reduce, collect, count, first, and s. Actions are executed and values of actions are stored back in Spark drivers or external storage systems.

Let's understand transformations in more detail:

  • reduce(func): This aggregates the elements of a dataset by executing a function on them. reduce works only with commutative and associative functions as it runs in parallel. For example, reduce could be taking (a, b) as the two inputs and having a+b as one output. Say if the input data is {1,2,…100}, using the sum function on reduce would result in {5050}, which is the sum of all the elements of the dataset.
  • collect(): This returns all the elements in a dataset. This is the equivalent of select * in SQL. For example, if the dataset...