Infusion of artificial intelligence into applications

When you infuse artificial intelligence into your applications, you enable real-time decision making, which significantly reduces latency. You can do infusion on IBM zSystems, including on IBM® z16™ with IBM Integrated Accelerator for AI, because these products optimize machine learning and deep learning algorithms.

What is AI and why use it on IBM zSystems?

Artificial intelligence (AI) is broadly used in technology to describe solutions that can learn on their own. Machine learning (ML) is a subset of AI and encompasses algorithms that make predictions by applying statistical methods to identify patterns in past behavior. Deep learning (DL), which is a subset of machine learning, uses neural networks that can, when exposed to different situations or patterns of data, learn on their own. Deep learning refers to a neural network that consists of more than three layers, including the input and the output. You can learn about typical use cases with AI on IBM Z® at Journey to AI on IBM zSystems and LinuxONE.

IBM zSystems and the IBM Integrated Accelerator for AI incorporated in IBM z16 optimize machine learning and deep learning algorithms. You can take advantage of these capabilities when you use suitable AI models with WebSphere® Application Server for z/OS®,

Why infuse AI into applications?

Infusing AI is about being able to apply AI across your enterprise, drawing on predictions, automation, and optimization to improve your business decisions and outcomes. It is also about putting AI into your business processes.

By infusing AI models into applications that run on WebSphere Application Server for z/OS, you enable real-time decision making within the transactions. This infusion significantly reduces latency that occurs when calls are made to off-platform functions, and avoids the need for the data to leave the platform. The data that is input to AI models is often relevant only at the time that the request is being processed, such as in the following examples:

Should this customer be approved for a loan now?
Does the customer's current circumstances make them eligible for a better insurance rate?
Can this insurance claim be fraudulent?

How can you infuse AI into applications?

Various methods for infusing AI into your applications exist. Which method works best for you depends on a number of considerations, including the following ones:

What AI model is selected and where it is deployed.
The response time that your transactions require.
The tools and products that your enterprise already uses.

The following methods are some of the most common ones for infusing AI into applications:

IBM Watson® Machine Learning for z/OS (WML for z/OS): You can use a Java™ API to call the WML for z/OS scoring feature that is configured in the same or a different application server.
IBM Operational Decision Manager with WML for z/OS: You can start an enhanced IBM Operational Decision Manager rule from the application to reference a model that is deployed to WML for z/OS and use the prediction from the model in the rule.
A community-available AI framework: You can use a community-available AI framework, such as IBM Snap Machine Learning (Snap ML), TensorFlow, or PyTorch. You can make a REST API call from the application to an AI model deployed to the AI framework. The AI framework must be hosted either in a z/OS Container Extension (zCX) within the z/OS environment, or in Linux® on IBM Z.

For more information about each method and how to choose among them, see Planning AI infusion into applications on IBM zSystems.