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

10. Introduction to Data Storage on Cloud Services (AWS)

Activity 10.01: Transforming a Table Schema into Document Format and Uploading It to Cloud Storage

Solution

  1. Create a directory called Activity10.01 in the Chapter10 directory to store the files for this activity.
  2. Open your Terminal (macOS or Linux) or Command Prompt (Windows), navigate to the Chapter10 directory, and type in jupyter notebook.
  3. In the Jupyter Notebook, click the Activity10.01 directory, create a notebook file with a Python 3 kernel, and add the following code:
    import os
    import json
    import boto3
    import shutil
    import pandas as pd
    # set your bucket name here
    # 'ch10-data' is NOT your bucket. It's just an example here
    # you should replace your bucket below
    BUCKET_NAME = 'ch10-data'
    # 1. download data from S3 bucket
    s3_resource = boto3.resource('s3')
    try:
        s3_resource.Bucket(BUCKET_NAME).download_file(
          &...