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

Machine Learning Algorithms

There are four main types of learning algorithms:

  • Supervised learning algorithm: This is trained to predict an outcome for a given set of input features. It's well studied and widely used in many areas such as spam classification, fraud detection, and product recommendation.
  • Unsupervised learning algorithm: This analyzes the underlying patterns or structure of data and groups data into clusters. Examples are outlier detection, fraud detection, and dimensionality reduction.
  • Semi-supervised learning: This falls between supervised learning and unsupervised learning. It's intended to boost learning accuracy for a supervised learning model by mixing unlabeled data.
  • Reinforcement learning algorithm: This is trained to play a "game." It learns to take a "smarter" action at each step in a game so that it will eventually win the game. Examples are AlphaGo, robot control, and quantitative trading.

This chapter...