Artificial Intelligence (#AI) are tools & techniques that enable machines/ #software to think with human-like #intelligence. Machine Learning (#ML) is a subset of AI that enables a computing system to learn with data without being explicitly programmed. Artificial Neural Networks (#ANN) are computing units inspired by neural networks of animal brains that form the core of machine learning systems.

It is interesting to note that techniques/ algorithms used for AI haven’t changed in the past 20+ years; however, the tools have changed. If you were an AI scientist just 10 years ago, you had to write code to build and train your neural networks, but not today. Gone are the days when you have to develop such a stack from scratch — now you have ML platforms, libraries, computing, and data platforms readily available as software platforms. Some of these capabilities are also available as a service that you can consume directly. This post provides an overview of such a stack and different consumption models available to consume AI capabilities as a Service.

Modern AI Stack

Modern AI #Stack consists of two components — infrastructure and developer environment.

Infrastructure refers to the tools, platforms, and techniques used to run store data, build and train AI/ ML algorithms, and the algorithms themselves.

Developer Environment refers to the tools that assist in developing code to bring out AI capabilities.

LOB applications and services are technically not part of the AI Stack. They derive value from the AI Stack.