Achieve real-time fraud 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.

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.

By training a model on risk exposure and co-locating it with transactional workloads, unlock insights about risk on a transaction-to-transaction basis.

Co-locate an inferencing model for analyzing loan applications, alongside the automation of rules-based business decisions, to achieve rapid insights, low latency, and minimal exposure of customer and lender data.

Co-locate an inferencing model for analyzing correlations between weather patterns and insurance risk alongside transactional systems, allowing you to achieve rapid insights.

Enable accurate analysis of satellite imagery with a deep learning learning model deployed to the IBM Z platform, ensuring the sensitive care of aerial images while driving rapid insights.

Achieve effective and energy-efficient computer vision for medical imaging through the training and inferencing of AI models on the IBM Z platform.

MLz requires other IBM and open source technologies. The minimally required technologies include:

• IBM z17, z16, z15™, z14, z13®, or zEnterprise® EC12 system
• z/OS 2.5 or 2.4
• z/OS UNIX System Services configured
• z/OS Integrated Cryptographic Service Facility (ICSF)
• z/OS OpenSSH
• IBM 64-bit SDK for z/OS Java Technology Edition Version 8 SR7, or Version 11.0.17 or later
• IBM WebSphere Application Server for z/OS Liberty version 22.0.0.9 or later
• Python AI Toolkit for IBM z/OS (for MLz Enterprise Edition)

Note: For more details, see Installing prerequisite hardware and software for ML for IBM z/OS.

MLz performs the best when adequate system capacity in terms of server, processor, memory, and disk space is available. Allocate sufficient capacity for MLz on your z/OS system to meet the demands of your enterprise machine learning workload.

Note: Additional capacity may be needed depending on your AI workloads. This may be influenced by the number of concurrent training jobs, the type of models being trained, the size of the training data set, the number of data features, and the specification of model parameters. For guidance on planning capacity for your training workloads, view Planning system capacity for ML for IBM z/OS: Capacity consideration for training workload on Z.

There are a number of terms that you will encounter when configuring MLz. To ensure a clear configuration process, it is essential to understand these terms. View Commonly used terms and definitions for installation and configuration for definitions.

A z/OS security administrator must configure a RACF KeyRing and a corresponding MLz setup user ID. For convenient access control, it is recommended that you this same user ID for MLz services.

For details about configuring user IDs, see Configuring user ID for setting up Machine Learning for IBM z/OS Enterprise and Configuring additional user IDs.

For details about configuring RACF, see Configuring a keyring-based keystore (JCERACFKS or JCECCARACFKS) for MLz.

A z/OS network administrator must set up a number of required and optional ports. Ports communicate across systems and services.

For more details, view Configuring ports for ML for IBM z/OS.

A z/OS database administrator must plan for the storage of metadata objects in the repository of your choice. This can be an embedded database with MLz or a Db2 for z/OS database.

For more details, view Creating metadata objects for the repository service.

You must select one of two options for storing important metadata about models. Selecting the embedded MLz database doesn’t require further configuration, but limits the availability of MLz services. Selecting Db2 for z/OS allows for high availability, but requires that you have a ready Db2 system, and also requires that you provide network, authentication, and schema details for MLz metadata objects.

You must specify host system details for both the MLz UI and core services, as well as credentials for the default admin user and the MLz application developer. You can also enable audit trace for governance of model data, model versions, and model deployments. Lastly, if you had selected Db2 for z/OS as your metadata repository, you can add MLz core services to a cluster, which routes services to an alternate MLz instance if one isn’t available.

You must create a name for the MLz runtime environment, as well as provide details for both the Spark runtime engine and the Python runtime engine. The Spark runtime engine is a high-performance analytics engine for large-scale data processing able to perform in-memory computing, and you will need to allocate various ports for its configuration, as well as determine the maximum number of retries for port binding. You will also have the option of enabling client-side Spark authentication. The Python runtime environment allows for the use of a Python-based scoring service, as well as the enablement of a Jupyter Notebook server for model development in the MLz UI. For Python, you will need to specify the Python virtual environment name and the Python packages installation source.

You must provide a name for the MLz scoring service. A scoring service is where your AI models are deployed to for real-time inferencing. You can choose to create a standalone scoring instance, or to set it to a cluster for high availability. Creating a cluster scoring services requires that you had selected Db2 for z/OS as your metadata repository, and will require you selecting an available cluster to join, or creating a new one by providing network details for the sysplex distributor.

You must choose whether to enable Trustworthy AI features for your MLz instance. If enabled, you will need to provide network and authentication details for an IBM z/OS Container Extensions (zCX) instance to support the features.

You can import models to MLz directly through the UI from your local system or from IBM watsonx.ai:

• If importing from file, you will need to provide a name for the file to be displayed in the MLz UI, and you will need to select the model type.
• If importing from watsonx.ai, you will need to provide connection and authentication details.

You can create models in MLz with a Jupyter Notebook server which can be accessed directly in the UI.

To create a model in Jupyter notebook editor, see Developing a model in the integrated Notebook Editor.

As data changes over time, model quality and prediction accuracy can degrade. As a result, it is important to continually monitor model accuracy and performance, particular when leveraging machine learning and deep learning algorithms.

In MLz, you can create model evaluations that asses a model’s predictive accuracy. These evaluations can run ad hoc or be scheduled to run repeatedly. For more details, see Evaluating and reevaluating a model.

Trustworthy AI offers explanations for individual AI predictions, providing transparency about the decision making process.

To enable Trustworthy AI in MLz, you can create a monitor. Each monitor can be tailored for local explainability or drift detection. Follow the instructions for Creating a monitor for more details.

Beginning in MLz Enterprise Edition v.3.1., you can generate visualizations for explainability and drift results. These visualizations show change over time and enhance understanding of the model and its performance. For more details, see Viewing explanation results for the monitor and Viewing drift results for the monitor.

Check out more Trustworthy AI features in Trustworthy AI.

For CICS, the MLz scoring engine can be hosted in a WebSphere Liberty server within the CICS runtime and provides a program ALNSCORE that can be invoked with EXEC CICS LINK, passing the data using CICS channels and containers.

For further detail, view Planning AI infusion into applications on IBM Z.

For IMS, the MLz WOLA (WebSphere Optimized Local Adapter) scoring interface can be invoked from IMS COBOL applications for high performance real-time inferencing in IMS online transactions.

Refer to the Deploy AI models for real-time inferencing article for further detail.

For WebSphere, a Java API can be used to call the MLz scoring feature configured in a WebSphere server.

For further detail, view Planning AI infusion into applications on IBM Z.

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.

Many CICS and IMS applications already use ODM rules to inform their decisions, in which case a rule called by the application can be enhanced with machine learning to drive an AI model deployed to MLz. If the application does not currently use ODM rules, it can 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.

For further detail, view Planning AI infusion into applications on IBM Z.