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IBM Launches Machine Learning for z System Mainframes

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Machine learning platform enables data scientists to automate the creation, training and deployment of operational analytic models

IBM on Feb. 15 launched a new product that should fit in nicely with its Watson artificial intelligence service inside a mainframe-based private cloud environment: IBM Machine Learning.

The company describes this as the “first cognitive platform for continuously creating, training and deploying a high volume of analytic models in the private cloud at the source of vast corporate data stores.” If this all works the way IBM thinks it will, it could help spur a resurgence in the use of its mainframe systems.
 
To do this, IBM has extracted the core machine-learning intellectual property from IBM Watson and will initially make it available specifically for z System mainframes. These are the operational centers of global organizations where billions of daily transactions are processed by banks, retailers, insurers, transportation firms and governments.

IBMWatson.

Automates Creation, Deployment of Analytical Models

IBM said that its Machine Learning platform enables data scientists to automate the creation, training and deployment of operational analytic models that will support: Any language (eg. Scala, Java, Python); any popular machine learning framework, such as Apache SparkML, TensorFlow, or H2O; any transactional data type; and without the cost, latency or risk of moving data off premises.
 
For the first time, IBM Machine Learning deploys Cognitive Automation for Data Scientists from IBM Research to assist data scientists in choosing the right algorithm for the data by scoring their data against the available algorithms and providing the best match for their needs. The service also considers various circumstances, such as what the algorithm is needed to do and how fast it needs to produce results, IBM said.
    
Use cases IBM sees include:

  • In retail, a sales forecasting system must take into account today’s market trends, not just those from last month. And, for real-time personalization, it must account for what happened as recently as 1 hour ago.
  • In financial services, a product recommendation system for a financial advisor or broker must leverage current interests, trends, and market movements, not last months.
  • In health care, personalized offerings must be tailored to an individual and their unique circumstance. Health care and personal fitness devices connected via the internet of things, can be used to collect data on human and machine behavior and interaction.

IBM claims its z Systems mainframe is capable of processing up to 2.5 billion transactions in a day. IBM Machine Learning for z/OS helps extract greater value from z Systems data without moving the data off the system for analysis.

IBM Machine Learning will first be available on z/OS and will be available for other platforms in the future, including IBM POWER Systems, the company said.

To learn more about IBM Machine Learning, go here. To learn more about the IBM z Systems portfolio, go here.

Originally published on eWeek