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.

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The Artificial Intelligence Infrastructure Workshop
Chinmay Arankalle | Gareth Dwyer | Bas Geerdink | Kunal Gera | Kevin Liao | Anand N.S.
Aug, 2020 | 732 pages
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Table of Contents (14 chapters)
Preface
About the Book
1
1. Data Storage Fundamentals
1. Data Storage Fundamentals
Introduction
Problems Solved by Machine Learning
Optimizing the Storing and Processing of Data for Machine Learning Problems
Diving into Text Classification
Looking at Terminology in Text Classification Tasks
Designing for Scale – Choosing the Right Architecture and Hardware
Using Vectorized Operations to Analyze Data Fast
Summary
Free Chapter
2
2. Artificial Intelligence Storage Requirements
2. Artificial Intelligence Storage Requirements
Introduction
Storage Requirements
Data Layers
Raw Data
Historical Data
Streaming Data
Analytics Data
Model Development and Training
Summary
3
3. Data Preparation
3. Data Preparation
Introduction
ETL
Data Processing Techniques
Streaming Data
Summary
4
4. The Ethics of AI Data Storage
4. The Ethics of AI Data Storage
Introduction
Summary
5
5. Data Stores: SQL and NoSQL Databases
5. Data Stores: SQL and NoSQL Databases
Introduction
Database Components
SQL Databases
MySQL
NoSQL Databases
MongoDB
Cassandra
Exploring the Collective Knowledge of Databases
Summary
6
6. Big Data File Formats
6. Big Data File Formats
Introduction
Common Input Files
Choosing the Right Format for Your Data
Introduction to File Formats
Summary
7
7. Introduction to Analytics Engine (Spark) for Big Data
7. Introduction to Analytics Engine (Spark) for Big Data
Introduction
Apache Spark
Apache Spark and Databricks
Understanding Various Spark Transformations
Understanding Various Spark Actions
Best Practices
Summary
8
8. Data System Design Examples
8. Data System Design Examples
Introduction
The Importance of System Design
Components to Consider in System Design
Examining a Pipeline Design for an AI System
Making a Pipeline System Highly Available
Summary
9
9. Workflow Management for AI
9. Workflow Management for AI
Introduction
Creating Your Data Pipeline
Challenges in Managing Processes in the Real World
Automating a Data Pipeline
Automating Asynchronous Data Pipelines
Workflow Management with Airflow
Summary
10
10. Introduction to Data Storage on Cloud Services (AWS)
10. Introduction to Data Storage on Cloud Services (AWS)
Introduction
Interacting with Cloud Storage
Getting Started with Cloud Relational Databases
Introduction to NoSQL Data Stores on the Cloud
Data in Document Format
Summary
11
11. Building an Artificial Intelligence Algorithm
11. Building an Artificial Intelligence Algorithm
Introduction
Machine Learning Algorithms
Model Training
Gradient Descent
Getting Started with PyTorch
Mini-Batch SGD with PyTorch
Summary
12
12. Productionizing Your AI Applications
12. Productionizing Your AI Applications
Introduction
pickle and Flask
Deploying Models to Production
Model Execution in Streaming Data Applications
Summary
Appendix
1. Data Storage Fundamentals
2. Artificial Intelligence Storage Requirements
3. Data Preparation
4. Ethics of AI Data Storage
5. Data Stores: SQL and NoSQL Databases
6. Big Data File Formats
7. Introduction to Analytics Engine (Spark) for Big Data
8. Data System Design Examples
9. Workflow Management for AI
10. Introduction to Data Storage on Cloud Services (AWS)
11. Building an Artificial Intelligence Algorithm
12. Productionizing Your AI Applications

Deploying Models to Production

After creating an API that contains your machine learning model, it has to be hosted in a production environment. There are several ways to do this, such as the following, for example:

  • By copying the API to a (virtual) server
  • By containerizing the API and deploying the container to a cluster
  • By installing the API in a serverless framework such as Amazon AWS Lambda or Microsoft Azure Functions

We’ll focus on the practice that is very common nowadays and still gaining popularity: containerizing the API and model.

Docker

AI applications usually work with large datasets. With “big data” comes the requirement for scalability. This means that models in production should scale in line with the data. One way to scale your software services is to distribute them in containers. A container is a small unit of computational power, similar to a virtual machine. There are many other advantages when containerizing your...

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