SSFZZH_3.2.0 - Documentation Index

Table of Contents

• Machine Learning for IBM z/OS Enterprise Edition v3.2.0 documentation
• Overview of MLz Enterprise
  • What's new in ML for IBM z/OS®
  • Security of ML for IBM z/OS
  • Supported browsers for ML for IBM z/OS user interfaces
  • Supported algorithms, data sources, data types, and model types
• Preparing for installation
  • Preparing for MLz installation
  • Roadmap for installing and configuring ML for z/OS Enterprise
  • Commonly used terms and definitions for installation and configuration
• Installing and configuring ML for IBM z/OS
  • Planning system capacity for ML for IBM z/OS
  • Obtaining SMP/E images, program directories, and maintenance packages for ML for IBM z/OS
  • Installing prerequisite hardware and software for ML for IBM z/OS
  • Installing ML for IBM z/OS
  • Configuring user ID for setting up Machine Learning for IBM z/OS Enterprise Edition
  • Additional configuration steps for ML for z/OS Enterprise
  • Configuring MLz to use CDA cloud credential storage
  • Configuring host services
  • Configuring ports for ML for IBM z/OS
  • Optional: Configuring Liberty server angel process for SAF authorization
  • Configuring secure network communications
    • Configuring AT-TLS for secure network connections with MLz
    • Configuring a keyring-based keystore (JCERACFKS or JCECCARACFKS) for MLz
  • Configuring MLz
  • Configuring MLz to connect to Db2 using an SSL secure port
  • Configuring ONNX compiler service
  • Configuring Python runtime environment
  • Configuring client authentication
  • Configuring ML for IBM z/OS scoring services
    • Configuring a ML for IBM z/OS scoring service by using the administration dashboard
    • Configuring a ML for IBM z/OS scoring service by using shell scripts
    • Configuring a ML for IBM z/OS scoring service with WOLA enabled
      • Configuring CICS for invoking scoring through WOLA
      • Configuring the Liberty server angel process
      • Creating SAF profiles for WOLA
      • Configuring the MLz scoring service
      • Configuring batch job for invoking scoring through WOLA
      • Configuring IMS for invoking scoring through WOLA
  • Configuring ML for IBM z/OS scoring services in a CICS region
  • Configuring scoring services for high availability
  • Configuring a ML for IBM z/OS cluster for high availability
  • Configuring a Model development environment
  • Verifying the installation and configuration of the ML for IBM z/OS
• Migrating ML for IBM z/OS to Enterprise edition v3.2.0
  • Upgrade from MLz Core edition to MLz Enterprise edition
  • Migrating ML for IBM z/OS® high availability cluster to Enterprise edition v3.2.0
• Administering
  • ML for IBM z/OS administration dashboard
    • Adding a scoring service
    • Adding a runtime environment
    • Enabling Dual Control for Machine Learning for IBM z/OS
    • Updating your Db2 password for the ML for IBM z/OS repository service
    • Updating audit trace settings for the ML for IBM z/OS services
    • Managing Notebook servers
  • z/OS started tasks
    • Creating started tasks for ML for IBM z/OS scoring services
    • Creating started tasks for MLz UI service and core services
    • Creating started tasks for the Spark cluster
    • Creating started tasks for the ONNX compiler service
    • Creating started tasks for the Model development environment
    • Creating started tasks for the Trustworthy AI service
  • Managing MLz users and privileges
  • MLz instance name
  • Cloning a ML for IBM z/OS instance
  • Creating metadata objects for the Machine Learning for IBM z/OS Enterprise Edition repository service
  • Updating the Java version of the Machine Learning for IBM z/OS® instance
  • Stopping and starting services running on ML for IBM z/OS
  • Updating ML for IBM z/OS runtime environments
  • Customizing the prefix of a ML for IBM z/OS service jobname
  • Updating the keystore for user authentication
  • Updating audit trace settings for the ML for IBM z/OS services
• Managing and developing models with ML for IBM z/OS
  • Importing a model from file
  • Importing a model from IBM watsonx.ai
  • Importing an ONNX model
  • Preparing data for a model in Db2 for z/OS
  • Developing a model in the integrated Notebook Editor
  • Generating sample code using the utility panel
  • Deploying and testing a model
  • Deleting models in Machine Learning for IBM z/OS®
  • Deleting scoring services in Machine Learning for IBM z/OS®
  • Deleting deployments in Machine Learning for IBM z/OS®
  • Deploying a model for batch scoring
  • Evaluating and reevaluating a model
  • Validating a model with IBM watsonx.governance
  • Creating custom libraries and runtimes
  • Generating Java helper classes for WOLA and CICS integrated ALN scoring APIs
  • Preparing a model for online scoring with CICS program ALNSCORE
  • Preparing a model for online scoring with CICS program ALNSRVSC
  • Preparing a model for online scoring in COBOL program using a Deployment ID with WOLA
  • Preparing a model for online scoring in COBOL program using a Serving ID with WOLA
  • Verify the WOLA scoring interface using batch job
• Trustworthy AI
  • Configuring Trustworthy AI
  • Configuring the ML model for Trustworthy AI
  • Creating a monitor
  • Configuring Trustworthy AI capabilities for the monitor
  • Viewing explanation results for the monitor
  • Viewing drift results for the monitor
  • Viewing monitor history
• MLz Enterprise APIs
  • Repository library APIs
  • ML for IBM z/OS model utility API
  • MLz Core APIs
    • MLz core services REST API specification
      • User management service APIs
        • Token generation API with client certificate
        • Token generation API with username and password
      • Spark integration service APIs
        • Submit job API
        • Get job status API
        • Get job logs API
      • Model management APIs
        • Get models API
        • Get model versions API
        • Get model version API
        • Delete model API
        • Import model API
        • Import model version API
        • Update model metadata with model patch API
      • Deployment APIs
        • Add scoring service API
        • Add scoring service node API
        • Get scoring service list API
        • Create online deployment API
        • Find deployment by name API
        • Get deployment detail API
        • Get approvals API
        • Post approvals API
        • DELETE approvals API
        • Update online deployment API
        • DELETE deployment API
        • Retrieve deployment swagger doc API
      • Host Services APIs
        • Create service host API
        • Get service hosts API
        • Get service host API
        • Search service hosts API
        • Update service host API
        • Delete service host API
        • Service host connection test
        • Service host generate certificates
        • Create service API
        • Get services API
        • Get service API
        • Search services API
        • Update service API
        • Delete service API
        • Get service health API
        • Service actions API
        • Service container actions API
        • Service recovery API
      • Scoring APIs
        • Online scoring API
        • Scoring service heartbeat API
      • Custom runtime APIs
        • Create custom library metadata API
        • Upload custom library content API
        • Create custom runtime API
        • Get custom libraries API
        • Get custom library API
        • Get custom runtimes API
        • Get custom runtime API
        • Update custom library API
        • Update custom runtime API
        • Delete custom library API
        • Delete custom runtime API
      • System level APIs
        • Get system level APIs
          • Get system level state API
          • Get system level API
        • Activate function level API
      • Audit trace APIs
        • Get audit traces API
        • Get audit traces with ID API
    • Usage scenarios of MLz core services APIs
      • Managing MLz users and privileges
      • Submitting application to Spark for model training
        • Submitting Spark application using RESTful APIs
        • Developing and submitting application using client mode
      • Saving and Managing models
      • Creating and managing deployments
        • Creating deployment for online scoring
      • Using custom runtime for Spark models
      • Making online predictions
  • MLz Trustworthy AI APIs
    • Trustworthy AI service heartbeat API
    • Token generation API with username and password
    • Create Monitor API
    • Update Monitor API
    • Get Single Monitor API
    • Get All Monitors API
    • Drift Training API
    • Create Explanation API
    • Get Single Explain Result API
    • Get All Explain Results API
    • Delete Explain Result API
    • Search and Filter transactions API
    • Drift Evaluation API
    • Delete Drift Result API
    • Get Single Drift Result API
    • Get All Drift Results API
    • Explainability Training API
    • Delete Monitor API
  • Downloading scoring service API specification
• Troubleshooting ML for IBM z/OS
  • Resolving the untrusted CA certificate error while opening the Model development environment in the Firefox browser
  • Resolving memory errors during model conversion
  • Resolving invalid input issues when scoring PMML models
• ALN error messages
  • Script error messages
  • UI error messages
  • TrustAI explanations error messages
  • ONNX compiler error messages
  • Online scoring error messages
  • Core Services error messages
  • DB Driver error messages
  • MLz utilities error messages
  • Configuration Tool error messages
• Glossary
• Program directory