Welcome to the Journey to AI on IBM Z and LinuxONE content solution, your homepage for technical resources.
With the introduction of an integrated accelerator for AI, organizations can scale their inferencing of transactions running on IBM z16 and IBM LinuxONE Emperor 4 while continuing to meet latency and throughput requirements. Now, AI can be directly embedded with mission critical workloads for real-time insights. Use cases from fraud detection, to risk analysis and natural language processing can be optimized like never before, allowing users to extract value from every transaction.
Explore this page to see the various use cases for AI on IBM Z and LinuxONE Emperor 4, the technologies involved, and next steps for getting hands-on with each solution.
Do you have questions about AI on IBM Z and LinuxONE? Want to know more?
Explore use cases and some relevant capabilities in the area of fraud prevention.
The problem
Fraud poses a significant risk to both consumers and businesses across a variety of industries, and creating an off-platform inferencing model to detect fraud can still leave companies exposed. The inherent latency introduces timeout issues—a significant blocker when you need to rapidly approve fair transactions. Thus, a significant portion of transactions may go unscored when trying to detect fraud.
The solution
Achieve real-time scoring through co-locating an AI model with the IBM Z applications managing your transactions. This enables you to analyze 100% of transactions running through your on-premises systems, and significantly reducing risk to both your business and customers.
The business impact
Drive significant savings in exposure risk, and significant improvements in customer relationships.
Learn more about achieving fraud detection with AI on IBM Z
A solution template is available to get started with this use case:
Access the solution templateThe problem
Fraudulent claims are an impediment both to insurers and consumers with legitimate claims. The claim auditing process can be manually intensive and time-consuming, making it difficult to scale prevention, and legitimate claims are often connected to an urgent need, making delays in processing a detractor from user satisfaction.
The solution
Eliminate the manually intensive auditing process through a machine learning model trained in detecting fraud for your claims processing systems. This enables you to rapidly analyze claims and significantly reduce overall human intervention.
The business impact
Drive a significant reduction in hours spent in the auditing process, keeping customers satisfied, and allowing you to allocate resources towards higher value tasks.
Coupling the IBM Accelerator for AI with a variety of capabilities makes fraud prevention at scale easier than ever.
The following capability is featured in this use case:
Machine Learning for IBM z/OS (MLz)
Enable the rapid deployment of an AI model built for fraud detection onto the IBM Z platform, allowing real time insights without compromising security, performance, or resiliency. MLz supports full model lifecycle management as well as capabilities for simplifying integration into business workloads.
The problem
With regulations around anti-money laundering varying by locality, it can be very difficult for financial institutions to remain compliant with federal policies. Moreover, it can be manually intensive to identify accounts that may be associated with money laundering activities, with business analysts often relying on the time and expertise of data science counterparts to train and deploy predictive models.
The solution
Enable the analysis of potential money laundering accounts through simple querying against historical data, allowing users to independently identify problematic accounts and take appropriate action.
The business impact
Drive transparent, easy-to-follow business processes that free up key data science resources for higher level needs, while rapidly identifying potential money laundering accounts and allowing for greater compliance with various regulatory bodies.
Click here to learn more about this solution
AI applications running on IBM Z can tackle money laundering through pattern recognition at scale, helping you identify similar payment accounts or currencies to that of a suspicious transaction.
The following capability is featured in this use case:
Use SQL queries to determine bank accounts with similar traits to a known money laundering account. SQL Data Insights on Db2 z/OS can deliver these insights without requiring a data scientist to develop a model.
The problem
Organized retail crime is a sizable burden for nearly all retailers, yet it is challenging to develop a successful model for analyzing the risk of retail crime when you only have access to your own transactional data. It's difficult to share models between merchants without potentially exposing the sensitive data of customers.
The solution
Using federated learning enables the sharing of models across retailers without exposing sensitive customer data, allowing all parties to build more effective models and improve their ability to detect bad actors.
The business impact
With federated learning, you gain opportunities to drastically improve machine learning models while contributed to a larger community of practice. In this specific use case, this enables you to more effectively combat retail crime, saving costs on inventory replenishment.
The following capabilities are featured in this use case:
IBM Watson Knowledge Catalog
Utilize a shared cloud-based metadata repository to access information to develop machine learning models for the recognition of retail crime patterns.
The problem
Fraud poses a significant risk to both consumers and businesses across a variety of industries, and creating an off-platform inferencing model to detect fraud can still leave companies exposed. The inherent latency introduces timeout issues—a significant blocker when you need to rapidly approve fair transactions. Thus, a significant portion of transactions may go unscored when trying to detect fraud.
The solution
Achieve real-time scoring through co-locating an AI model with the IBM LinuxONE applications managing your transactions. This enables you to analyze 100% of transactions running through your on-premises systems, and significantly reducing risk to both your business and customers.
The business impact
Drive significant savings in exposure risk, and significant improvements in customer relationships.
The following capabilities are featured in this use case:
Go, LightGBM, Python
LightGBM is a machine learning framework with python interfaces, which is used to develop and train a machine learning model - on or off the LinuxONE Emperor 4 platform. When the model is ready to be used for inference, it is deployed to LinuxONE Emperor 4 using and using a Go based serving application. This provides for a high speed and responsive fraud detection service that, on LinuxONE Emperor 4, can scale to meet demand.
The problem
Organized retail crime is a sizable burden for nearly all retailers, yet it is challenging to develop a successful model for analyzing the risk of retail crime when you only have access to your own transactional data. It's difficult to share models between merchants without potentially exposing the sensitive data of customers.
The solution
Using federated learning enables the sharing of models across retailers without exposing sensitive customer data, allowing all parties to build more effective models and improve their ability to detect bad actors.
The business impact
With federated learning, you gain opportunities to drastically improve machine learning models while contributed to a larger community of practice. In this specific use case, this enables you to more effectively combat retail crime, saving costs on inventory replenishment.
The following capabilities are featured in this use case:
IBM Cloud Pak for Data + IBM Watson Federated Learning + IBM Hyper Protect Virtual Servers
A multi-platform solution that enables the sharing of insights gathered from confidential data. Federated learning provides a mechanism to share insights across industry groups, fostering better collaboration, while not exposing confidential data outside of the owning organization.
Fraud prevention is especially notable for organizations using the following runtime environments. The examples described are not unique to each runtime, but are used to illustrate some of the many possibilities, and there are also a variety of technology solutions available. For more details, see 'Infusing AI into applications' in 'Learn more'.
A claims processing application running in
A financial application running in
An application running in
An electronic funds transfer system running on
Explore use cases and some relevant capabilities in the area of business process optimization.
The problem
The management of health insurance claims can be a highly manual process, and requires adherence to specific business policies and regulations for proper classification and response. Customer satisfaction can be negatively impacted due to delays in claims processing.
The solution
By training a model on previous insurance claims data and deploying it to z/OS, you can integrate the model with an insurance claims web application while benefitting from the speed and security of IBM Z.
The business impact
Drive significant savings resources required for claims processing as well as improvements in customer relationships.
A solution template is available to get started with this use case:
Access the solution templateThe problem
Many financial products, such as consumer loans, require an evaluation of the customer’s credit-worthiness before funds can be made available. If conducted manually, these processes can take be time-intensive and impact the customer experience. Moreover, misclassification of a customer’s credit risk can put the organization at financial risk.
The solution
A machine learning model can be trained on a credit risk assessment dataset that indicates whether a customer had defaulted alongside numerous other relevant factors. This model can be deployed to z/OS and integrated with a credit assessment application while benefitting from the speed and security of IBM Z.
The business impact
Through a credit risk assessment model deployed on z/OS, the evaluation of customer credit-worthiness can be better automated to drive rapid responses. This can improve the overall customer experience while safeguarding the organization from errors experienced during manual processing.
A solution template is available to get started with this use case:
Access the solution templateThe problem
When processing credit card transactions, it's critical to determine which trades or transactions are highly exposed to risk before settlement—but given the manual work involved, it can be difficult to optimize this work without potentially impacting service-level agreements.
The solution
By training a model on risk exposure and co-locating it with transactional workloads, you can unlock insights about risk on a transaction-to-transaction basis.
The business impact
Through the enhancement of a rules-based approach using the IBM Z platform, you can achieve a high level of analysis with no impact to service level agreements.
The following capabilities are featured in this use case:
PyTorch + IBM z/OS Container Extensions (zCX) + ONNX + Machine Learning for IBM z/OS (MLz)
PyTorch provides a deep learning framework which may be trained off the IBM Z platform by accessing data co-located on a zCX instance. The PyTorch model is then simply exported to the ONNX format and deployed using the MLz ONNX Scoring service. This provides an endpoint that can be called from the z/OS application.
Use sample code for exporting or converting a model to the ONNX format
The problem
The loan approval process can be lengthy and manual, and customers are expecting ever-faster response times—but introducing an approval model over a distributed system can increase the risk and exposure for both lenders and customers.
The solution
An inferencing model for analyzing loan applications, alongside the automation of rules-based business decisions, can be co-located alongside transactional systems to achieve rapid insights, low latency, and minimal exposure of customer and lender data.
The business impact
Compared to models running on distributed systems, a solution built on top of the IBM Z platform can minimize loan defaults through early fraud identification, accelerate credit approval processing, and improve overall customer service and profitability.
The following capabilities are featured in this use case:
Machine Learning for IBM z/OS (MLz) + IBM Operational Decision Manager
MLz enables the rapid deployment of an AI model built for loan analysis onto the IBM Z platform, allowing real time insights without compromising security, performance, or resiliency. IBM Operational Decision Manager allows the analysis, automation and governance of rules-based business decisions, allowing for optimizations in the loan authorization process alongside insights produced through the deployed model.
The problem
A defaulted loan is a problem for both the lender and borrower. Without the ability to measure the factors leading to default in real time, lenders are always exposed to the risk of unfulfilled payments.
The solution
Use an optimized querying process against historical transactional data to quickly and efficiently identify problematic accounts and plan appropriate action.
The business impact
By leveraging AI capabilities on the IBM Z platform, organizations can derive value from their historical transactional data in real time, and become more proactive and agile in risk mitigation practices.
The following capability is featured in this use case:
Use SQL queries to determine loans with similar traits to loans that have gone into forbearance. SQL Data Insights on Db2 z/OS can deliver these insights without requiring a data scientist to develop a model.
The problem
In highly competitive insurance markets, proactivity and speed can be key to ensuring strong customer relationships—but analyzing customer circumstances to reassign insurance rates can be a manual and time intensive process. With all the time spent determining whether new rates are appropriate, lenders are limited in how much they attention they can provide to all accounts.
The solution
Use an optimized querying process against historical transactional data to quickly and efficiently identify opportunities to provide improved rates to customers.
The business impact
Insurers can proactively reassign insurance rates based on changes to customer circumstances, enabling greater user satisfaction and reducing the overall manual responses required by the insurer.
The following capability is featured in this use case:
Use SQL queries to determine appropriate rates for potential customers with similar traits to existing, highly qualified accounts. SQL Data Insights on Db2 z/OS can deliver these insights without requiring a data scientist to develop a model.
The problem
It can be enormously complex and time intensive to understand how weather patterns should impact insurance products. With different considerations across different regions, this becomes a problem of scale—that is, ensuring you have the expertise and computing power required to conduct such analysis, all while continuing to work at the speed of customer expectations.
The solution
An inferencing model for analyzing correlations between weather patterns and insurance risk can be co-located alongside transactional systems, allowing you to achieve rapid insights.
The business impact
Insurers can size packages and evaluate exposure based meteorological data, enabling more accurate pricing and greater management of risk, while also rapidly delivering answers to awaiting customers.
The following capabilities are featured in this use case:
SciKit-Learn + Machine Learning for IBM z/OS (MLz)
SciKit-Learn allows you to train a machine learning model based on the categorization of insurance packages against weather patterns. MLz enables the rapid deployment of an AI model built for loan risk analysis onto the IBM Z platform, allowing real time insights without compromising security, performance, or resiliency.
Learn about accessing SciKit-Learn and other open access frameworks on IBM Z and LinuxONE
Business process optimization is especially notable for organizations using the following runtime environments. The examples described are not unique to each runtime, but are used to illustrate some of the many possibilities, and there are also a variety of technology solutions available. For more details, see 'Infusing AI into applications' in 'Learn more'.
A loan approval application running in
An insurance application running in
An insurance application running in WebSphere Liberty could, for example, enhance customer satisfaction by using an AI model hosted in MLz within the Liberty server to determine whether to offer a discount.
A flight booking application in
Explore use cases and some relevant capabilities in the area of image and text analysis.
The problem
Organizations across industries need insights about changes observed in satellite imagery to properly plan the use and development of land, however, poring over such imagery can be a manually intensive process prone to human error.
The solution
Enable accurate analysis of satellite imagery with a machine learning model deployed to the IBM Z platform, ensuring the sensitive care of aerial images while driving rapid insights.
The business impact
New thresholds of accuracy, scale, and operational costs can be achieved through running a machine learning model on the IBM Z platform, allowing teams to rapidly understand changes to a given space and expedite their decision-making process.
The following capabilities are featured in this use case:
TensorFlow + z/OS Container Extentions (zCX) + ONNX + Machine Learning for IBM z/OS (MLz)
TensorFlow provides a deep learning framework which may be trained off the IBM Z Platform by accessing data co-located on a zCX instance. The model is then simply exported to the ONNX format and deployed using the MLz ONNX Scoring service. This provides an endpoint that can be called from the z/OS application.
Use sample code for exporting or converting a model to the ONNX format
The problem
In any industry, it's a challenge to meet the support demands of customers at scale. With queries arriving at all times, and subject matter expertise required to resolve specific scenarios, it's clear that manual responses alone may not be enough to meet demands.
The solution
Natural language processing (NLP) can be leveraged in the creation of chatbots, and the data created in conversational interactions with AI can be stored in a highly secure on-premises server.
The business impact
By scaling the use of NLP and chatbots, organizations can meet the 24/7 support demands of their customers, increasing overall satisfaction in their services while ensuring the security of customer conversations with the IBM Z platform.
The following capabilities are featured in this use case:
huggingface + RASA
huggingface allows for the building, training, and deployment of state-of-the-art models powered by references to open source machine learning communities, while RASA allows for the automation of conversational experiences at scale.
The problem
Imaging is useful across various parts of the medical industry, including both in the diagnosis of patients and in insurance determinations. But with many organizations carrying large historical records of medical images, training a model to recognize patterns and anomalies can be resource intensive.
The solution
Effective and energy efficient computer vision for medical imaging can be achieved through the training and inferencing of AI models on the IBM Z platform.
The business impact
Organizations across the medical industry can achieve better accuracy, time to diagnosis, and energy efficiency, ultimately enabling greater care for patients and customers.
The following capabilities are featured in this use case:
IBM Deep Learning Compiler
Enables multiple deep learning frameworks to run on IBM z16
PyTorch + ONNX + Machine Learning for IBM z/OS (MLz)
PyTorch provides a deep learning framework which may be trained off the IBM Z Platform. The model is then simply exported to the ONNX format and deployed using the MLz ONNX Scoring service. This provides an endpoint that can be called from the z/OS application. This allows for real-time insights without compromising the security of patient data.
The problem
In any industry, it's a challenge to meet the support demands of customers at scale. With queries arriving at all times, and subject matter expertise required to resolve specific scenarios, it's clear that manual responses alone may not be enough to meet demands.
The solution
Natural language processing (NLP) can be leveraged in the creation of chatbots, and the data created in conversational interactions with AI can be stored in a highly secure on-premises server.
The business impact
By scaling the use of NLP and chatbots, organizations can meet the 24/7 support demands of their customers, increasing overall satisfaction in their services while ensuring the security of customer conversations with the IBM LinuxONE platform.
The following capabilities are featured in this use case:
Red Hat OpenShift, spaCy, RASA, TensorFlow
RASA provides an open source platform for conversational AI-like chat bots. RASA relies on highly optimized back-ends in frameworks like spaCy and TensorFlow, which can drive the execution of pre-trained Natural Language Processing (NLP) models. On LinuxONE Emperor 4, a pre-trained spaCy language model is imported to a RASA chatbot application that can provide insights to guide end users.
The problem
Imaging is useful across various parts of the medical industry, including both in the diagnosis of patients and in insurance determinations. But with many organizations carrying large historical records of medical images, training a model to recognize patterns and anomalies can be resource intensive.
The solution
Effective and energy efficient computer vision for medical imaging can be achieved through the training and inferencing of AI models on the IBM LinuxONE platform.
The business impact
Organizations across the medical industry can achieve better accuracy, time to diagnosis, and energy efficiency, ultimately enabling greater care for patients and customers.
The following capabilities are featured in this use case:
IBM Deep Learning Compiler, ONNX, PyTorch
PyTorch provides a deep learning framework which may be trained on or off the LinuxONE Emperor 4 platform. The model is then exported to the ONNX format and enabled on LinuxONE using the IBM Deep Learning Compiler, thus co-locating the model alongside key Linux workloads. This allows for real-time insights without compromising the security of patient data.
Image and text analysis use cases can be relevant to IBM Z runtime environments if part of the handling or processing of the images or text is carried out by applications running in one of
Explore use cases and some relevant capabilities in the area of intelligent infrastructure.
The problem
While organizations may rely on partnerships with vendors to analyze system diagnostic data, privacy regulations and post-processing speeds can create a significant blocker. With operational stability as a top priority, it's essential to get information in front of experts, while also taking care to protect customers' data.
The solution
Machine learning can be leveraged on the IBM Z platform to identify sensitive information and redact it from diagnostic dumps, ensuring that the resulting information allows for an inspection of system performance without creating unnecessary risk for customers.
The business impact
Organizations can be more agile in their operations with vendors and other supporting parties, allowing them to quickly produce diagnostic information without the work of manually removing PII. Thus, greater operational efficiency can be achieved on an ongoing basis.
The following capability is featured in this use case:
IBM Z Data Privacy for Diagnostics
Leverage facilities for tagging sensitive data and producing redacted diagnostic dumps which do not contain the tagged sensitive data.
Read the documentation for IBM Z Data Privacy for Diagnostics
There can be opportunities to introduce intelligence into your application infrastructure on IBM Z by leveraging AI models.
Available for IBM z16
Enable problem identification, isolation and resolution on IBM Z through analysis of structured and unstructured operational data.
IBM Z Application Performance Management Connect
Available for IBM z16
Track transaction information from z/OS subsystems to APM solutions.
IBM Operational Decision Manager
Available for IBM z16
Discover, capture, analyze, automate and govern rules-based business decisions on premises or on the cloud.
Available for IBM z16
Conduct cloud native development and testing for z/OS on IBM Cloud.
IBM Cloud Pak for Data on IBM Z
Available for IBM z16 and IBM LinuxONE Emperor 4
Confidently leverage your enterprise data within a secured, resilient IBM Z and LinuxONE private cloud infrastructure.
IBM
Available for IBM z16
Leverage real-time insight from data at the point of origin.
Available for IBM z16
Utilize unprecedented AI inferencing performance for every transaction while meeting SLAs.
Available for IBM z16
Uncover hidden insights in Db2 for z/OS data.
Available for IBM z16
Enhance database performance with machine learning.
IBM Z Data Privacy for Diagnostics
Available for IBM z16
Leverage machine learning to detect and redact PII from diagnostic dumps.
Available for IBM z16 and IBM LinuxONE Emperor 4
Deploy advanced, explainable AI across the ITOps toolchain.
Available for IBM z16
Proactively identify operational issues in log and metric data.
IBM Z Integrated Accelerator for AI and IBM Integrated Accelerator for AI on LinuxONE
Available for IBM z16 and IBM LinuxONE Emperor 4
A 7 nm microprocessor engineered to meet the demands our clients face for gaining AI-based insights from their data without compromising response time for high volume transactional workloads.
IBM Z and LinuxONE Container Image Registry
Available for IBM z16 and IBM LinuxONE Emperor 4
Utilize a channel of open source container images available for use, free of charge, that can be pulled and managed through the common graphical and command line interfaces that support containerized workloads.
Available for IBM z16 and IBM LinuxONE Emperor 4
ONNX is an open standard used for converting between different machine learning frameworks.
IBM Z Optimized for TensorFlow and IBM LinuxONE Optimized for TensorFlow
Available for IBM z16 and IBM LinuxONE Emperor 4
TensorFlow is a widely used end-to-end platform for machine learning and deep learning, including a large ecosystem of tools, libraries, and community resources.
Available for IBM z16 and IBM LinuxONE Emperor 4
PyTorch is an open-source machine learning library based on the Torch library, helping to accelerate the path from research prototyping to production deployment.
Available for IBM z16 and IBM LinuxONE Emperor 4
Keras is a Python-based deep learning library that functions as a high-level API specification for neural networks.
Available for IBM z16 and IBM LinuxONE Emperor 4
Utilize the leading open-source data science platform, including open-source package distribution and environment management, all with IBM Z and LinuxONE workloads.
The Integrated Accelerator for AI offers seamless exploitation for the IBM Z runtimes, in that upgrades to the runtime environment should not be required, and applications that are already leveraging suitable deep-learning AI models deployed to IBM Z can benefit from the acceleration without change. It also opens up new possibilities for applications to incorporate AI in their processing, benefiting from the reduced latency, and using data that might only be relevant while the transaction is running.
The following sections explore how each of the application runtimes can invoke AI models deployed to the various different frameworks and environments.
This use case works well for applications running in:
CICS TSIMS TM- IBM
WebSphere Application Server for z/OS (WebSphere traditional and WebSphere Liberty) - z/OS batch applications
From the application, you can make a call that invokes an AI model deployed to the MLz base running in z/OS or, in some cases, within the runtime itself.
Depending on the runtime, there are a number of ways in which the MLz scoring can be invoked:
- Using CICS API commands: For CICS TS, the MLz scoring engine can be hosted in a Liberty server within the CICS runtime, and invoked via an EXEC CICS LINK call that passes the data using CICS channels and
containers . - Using WebSphere Optimized Local Adapter (WOLA) APIs: For IMS COBOL applications, and as an alternative for COBOL applications running in z/OS Batch or CICS TS, WOLA APIs can be used with a MLz scoring server that has WOLA enabled.
- Using Java API: For WebSphere traditional or WebSphere Liberty, a Java API can be used to call the MLz scoring feature configured in the same WebSphere server as the application runs, or configured in a separate WebSphere server.
- Using REST API: For z/OS batch applications, and as an alternative for CICS TS, IMS TM, and WebSphere Application Server, the model can be invoked using a REST API.
This use case works well for applications running in:
CICS TSIMS TM- IBM
WebSphere Application Server for z/OS (WebSphere traditional and WebSphere Liberty) - z/OS batch applications
An ODM rule driven by the runtime application can be enhanced to reference a model deployed to MLz, and then use the prediction from the model in the rule. ODM uses a highly efficient interface between ODM and MLz.
If the application already uses ODM rules, then a rule called by the application could be enhanced with machine learning to drive an AI model deployed to MLz. If the application does not currently use ODM rules, it could be updated to use an ODM rule that drives the AI model via MLz, and hence include additional insight in the result from the rule. This use case has the added benefit of transparency in the use of the AI prediction, due to it being visible in the rule.
This use case works well for applications running in:
z/TPF CICS TSIMS TM- IBM
WebSphere Application Server for z/OS (WebSphere traditional and WebSphere Liberty) - z/OS batch applications
From the application, you can make a REST call to an AI model deployed in popular frameworks such as IBM Snap Machine Learning (SnapML), TensorFlow, or PyTorch, that might be hosted in a z/OS Container Extentions (zCX) instance within the z/OS environment, or hosted in Linux on IBM Z. When using zCX, the call uses an optimized form of access within z/OS. When using Linux on IBM Z, the call can use Shared Memory Communications (SMC) for efficient access.
The REST APIs provided by the AI model can be driven from the application in a number of ways, including:
- Using z/OS Connect EE: CICS, IMS and batch applications can take advantage of z/OS Connect EE and the API requester functionality to drive the REST APIs.
- Using Java libraries: WebSphere traditional and WebSphere Liberty applications can use Java libraries such as ‘Rest Client for MicroProfile’ to make REST API calls.
- Using z/TPF REST consumer support: z/TPF applications can make REST calls by using z/TPF REST consumer support.
- Using CICS API commands: CICS TS applications can use EXEC CICS WEB SEND and WEB RECEIVE commands.
- Using the z/OS client web enablement toolkit: IMS applications can take advantage of the z/OS client web enablement toolkit provided with z/OS, using the HTTP/HTTPS protocol enabler APIs to invoke the AI model and the JSON parser to interact with, create, or parse the corresponding JSON payloads.
Explore pathways for infusing AI with every business transaction and to automate your IT infrastructure through AI-infused applications
Learn the value of leveraging AI on the IBM Z and LinuxONE platform
Learn how to jumpstart your experience with AI on IBM Z
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.
Watch this video to see a demo on real-time detection and the prevention of credit card fraud with AI on IBM Z.
Determine business insights during transactions using IBM z16, harnessing the scalability and speed needed to address fraud.
Learn how to use AI application on IBM z16 to identify money laundering patterns and prevent them in real time.
Join a community of practitioners and experts using AI on the IBM Z and LinuxONE platforms
Transform and modernize your IT approach to turn valuable IBM Z data into business opportunities
IBM Db2 AI for z/OS (Db2ZAI) is built on the services that are provided by Machine Learning for IBM z/OS to help optimize performance.
Build unique AI solutions with step-by-step instructions and reference datasets
Sample code and hints for how you could utilize a real time data consumption strategy with your IzODA applications using the SMF Realtime Interface on z/OS
Choose Your Path: Spark and Scala or Anaconda and Python
Find samples, containers, and other relevant resources for using AI on the IBM Z platform
View sample code for running a credit card fraud detection scenario with AI on IBM Z
Access sample container build files for AI software that can be utilized in s390x environments.
Access small, useful examples to demonstrate some of the technologies available for use on IBM Z and LinuxONE systems.
Technical guide for planning the infusion of AI with applications running in CICS, IMS, WebSphere Application Server for z/OS, and z/TPF.
Deploying AI models for real-time inferencing in IMS transactions with the MLz 2.4 new feature using WOLA.
Embedding the Machine Learning for IBM z/OS scoring service in a CICS region using the MLz ALNSCORE program.
Redpaper on Optimized Inferencing and Integration with AI on IBM Z - shows a CICS application using a model in MLz to predict a credit risk score
View a tutorial that steps through enhancing ODM rules with Machine Learning for IBM z/OS predictions
Learn how to call REST APIs from z/OS applications using z/OS Connect
View a Github sample showing a REST API call from IMS using z/OS Connect EE
View a Github sample of a CICS COBOL and IMS COBOL application that uses the API Requester function of z/OS Connect EE
Use the z/OS client web enablement toolkit provided with z/OS to interact with the REST APIs provided by AI models
Use the z/TPF client to call a REST service from a z/TPF application
Derive new insights and advantages from each transaction by charting your journey to open data analytics.
Updated links throughout the page to reflect the latest level of documentation for each product.
The use cases and products/related solutions showcased on this page have been updated to reflect the announcement of the IBM LinuxONE Emperor 4.
Information about the open beta for IBM Z Optimized for TensorFlow has been added to the 'Featured open source communities' section.
The technical resources section has been updated to include a technical guide for planning the infusion of AI with applications running in CICS, IMS, WebSphere Application Server for z/OS, and z/TPF.
The page has been updated to feature a host of use cases broken out across the 'Get Started' section, with additional considerations for relevant runtimes provided.
Additionally, two new 'Learn more' accordions have been added, one featuring the various products and capabilities available for AI on the IBM Z platform, and the other featuring methods for implementing AI with relevant runtimes.
Lastly, a host of new resources have been added, including new demonstration videos for featured use cases, product documentation, and solution briefs.
AI and Data Science availability on IBM Z