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

Model Training

There are many different machine learning models for each of those four types of learning algorithms. Machine learning models rely on some forms of mathematical/statistical models. When we train models, it means we use an algorithm to find out the model's unknown parameters. Scientifically speaking, we cannot definitively find out the ground truth for unknown parameters. Instead, we can only estimate the unknown parameters as closely as possible to the ground truth by using mathematical/statistical methods on sample data. Estimating unknown model parameters is equivalent to solving a mathematical equation whose solution comes in one of two forms: closed or non-closed.

Closed-Form Solution

Some algorithms' mathematical models have closed-form solutions. A model with a closed-form solution can be solved by expressing the model parameters analytically in terms of a finite number of certain "well-known" functions. A classic example is a linear regression...