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

6. Big Data File Formats

Activity 6.01: Selecting an Appropriate Big Data File Format for Game Logs

Solution

  1. In the Chapter06 directory, create the Activity06.01 directory to store the files for this activity.
  2. Move the session_log file into the Chapter06/Data directory.
  3. Open your Terminal (macOS or Linux) or Command Prompt window (Windows), move to the installation directory, and open the Spark shell in it using the following command:
    spark-shell --packages org.apache.spark:spark-avro_2.11:2.4.5

    You should get the following output:

    Figure 6.27: Spark shell

    By using this command, the Spark shell will be launched and we will now load the dataset from the CSV file.

  4. Load the session_log.csv dataset:
    val df_ses_log_csv = spark.read.options(Map("inferSchema"-  >"true","delimiter"->",","header"-  >"true")).csv("F:/Chapter06/Data/session_log.csv")

    Note

    Update the input path of the file according...