AI and Analytics: Business Insight

Enable self-service, trusted, real-time data access, and embed AI deep into the enterprise transaction systems.

Overview

Infuse AI in real-time into every business transaction, driving top-line growth and bottom-line savings, for your mission critical applications while meeting the most stringent SLA’s. Leverage both IBM and open-source solutions to enable your data scientists and engineers to use the applications they know and trust.

Value

20x
Lower inferencing response time vs sending the same inferencing operations off platform 1
19x
Higher throughput with inferencing vs sending the same inferencing operations off platform 1
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Journey

AI on IBM Z and LinuxONE

Learn about the benefits of leveraging IBM Z analytics and AI for insights.
Video

Uncover the potential of your enterprise data with AI on IBM Z

Learn how AI on the IBM Z platform enables new insights and business opportunities.
Blog

Expert perspectives

Learn about the principles and investments driving IBM Z’s role as a world-class inference platform.
Document

Perform real-time analytics and ML on IBM Z

White paper on performing real-time analytics and machine learning on IBM Z
Blog

AI Open-Source Software and IBM Z

Read how IBM Z and LinuxONE provide a secure high-performance environment with an ecosystem of open-source software to bring AI, ML, and DL to existing transactional applications...
Website

Journey to AI on IBM Z and LinuxONE content solution

Learn how AI on Z and LinuxONE leverages AI technology and open source frameworks to help you to build and train models anywhere (including Z) and deploy them on Z and LinuxONE...
Journey

Fraud prevention

Real-time detection and prevention of credit card fraud with AI on IBM Z
Blog

Real-time detection and prevention of credit card fraud with AI on IBM Z

AI solutions on IBM Z and LinuxONE can help curtail credit card fraud before it happens
Blog

Improving fraud detection with TensorFlow

Read about how machine learning/deep learning frameworks help detect credit card fraud
Document

Learn about the AI capabilities and methods available to combat fraud with IBM Z

Solve fraud scenarios in real time with AI solutions on IBM Z and LinuxONE.
Website

Get started with fraud detection on IBM Z

Navigate our various fraud use cases for AI on IBM Z in our Content Solution, the technologies involved, and next steps for getting hands-on with each solution.
Demo

Use Watson Machine Learning for z/OS to detect fraudulent transactions

Watch a demo on real time detection and prevention of credit card fraud with AI on Z.
Journey

Anti Money Laundering

Using AI applications running on IBM Z, not only identify various money laundering patterns but also prevent them from happening in real-time.
Document

Learn the AI capabilities and methods available for AML on IBM Z

Solve Anti Money Laundering with AI solutions on IBM Z and LinuxONE.
Website

Get started with planning AML on IBM Z

Navigate to Anti Money Laundering under our fraud use cases for AI on IBM Z, the technologies involved, and next steps for getting hands-on with each solution.
Demo

Use Db2 for z/OS with SQL Data Insights for AML

Watch a demo on how AI applications running on IBM Z can tackle the money laundering problem in various ways including solving the scatter gather problem.
Journey

Image Recognition

Learn about how customers can use image recognition with AI solutions on IBM Z and LinuxONE.
Document

Learn the capabilities and methods available for image recognition on IBM Z

Learn about the benefits of using image recognition with AI solutions on IBM Z and LinuxONE.
Website

Get started with planning image recognition on IBM Z

Navigate to our image and text analysis use cases for AI on IBM Z, the technologies involved, and next steps for getting hands-on with each solution.

Footnotes

  • IBM z16 with z/OS delivers up to 20x lower response time and up to 19x higher throughput when co-locating applications and inferencing versus sending the same inferencing operations to a compared x86 cloud server with 60ms average network latency.*Disclaimer: Performance result is extrapolated from IBM internal tests running local inference operations in a z16 LPAR with 48 IFLs and 128 GB memory on Ubuntu 20.04 (SMT mode) using a synthetic credit card fraud detection model (https://github.com/IBM/ai-on-z-fraud-detection) exploiting the Integrated Accelerator for AI. The benchmark was running with 8 parallel threads each pinned to the first core of a different chip. The lscpu command was used to identify the core-chip topology. A batch size of 128 inference operations was used. Results may vary.