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Latest from the Lab: What's New Machine Learning Sam Buhler - Machine Learning Product/Offering Manager


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Join us on October 10th to hear all of the latest news about the IBM z Analytics Portfolio. The development and lab teams have been incredibly busy with tons of exciting announcements covering Db2 for z/OS, and the Db2 Tools & Utilities. We will also highlight something very special for the Db2 Analytics Accelerator, which continues to bring unprecedented analytics capabilities to the mainframe. We will debut more innovation with QMF analytics and reporting. IBM will be announcing new breakthrough capabilities in the data virtualization space. Finally, we will cover the revolutionary Machine Learning solution which is redefining what's possible with transactional scoring and analytics. Join us for this packed summary of new capabilities that can help your data & analytics requirements.

Jeff Josten - Db2 for z/OS Chief Architect - IBM Distinguished Engineer
Udo Hertz - Director of Analytics Development Boeblingen

Steve Mink - Worldwide z System Analytics Client Success

Sam Buhler - Machine Learning Product/Offering Manager

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Latest from the Lab: What's New Machine Learning Sam Buhler - Machine Learning Product/Offering Manager

  1. 1. © 2017 IBM Corporation Latest from the Lab: What's New Machine Learning Sam Buhler - Machine Learning Product/Offering Manager
  2. 2. © 2017 IBM Corporation2 Please Note • IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. • Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. • The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. • The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. 2
  3. 3. © 2017 IBM Corporation3 Machine Learning for z/OS Quick model development, deployment and management = Faster ROI Convert insight into opportunity and opportunity into revenue Data Scientist Application Developer ML & Production Engineers Hybrid cloud approach to model lifecycle management & collaboration § Platform agnostic model development § Leverage enterprise grade open source software § Real-time insight embedded with transactions § Insight incorporated from any platform § Industry leading encryption, security, reliability & availability
  4. 4. © 2017 IBM Corporation4 IBM Z Analytics Roadmap IBM z/OS Platform for Apache Spark IBM Machine Learning for z/OS Optimized Data Layer Advanced Analytics Solutions Data Analytics Processing engine, languages/run-times, and libraries to enable analytics solutions Infrastructure and Accelerators z13 Db2 Analytics Accelerator 1H2017 2H2017 IBM Open Data Analytics for z/OS IBM Machine Learning for z/OS Optimized Data Layer z14 Db2 Analytics Accelerator vIDAA* * Virtual Accelerator 3Q SOD • Exploitation of Python • Full-lifecycle management of PMML models • Improved model governance • DSX Enhancements & Integration • IBM z14 – security, more memory, new instructions, zHyperLink, new SW pricing • Next generation Accelerator and Virtual Db2 Analytics Accelerator appliance Db2 for z/OS • Anaconda and Python runtimes and libraries • Improved data access
  5. 5. © 2017 IBM Corporation5 Data Ingest Data Processing • Auto Data Preparation+ • Visualization libraries Model Training • Auto modelling (CADS/HPO) • Jupyter Notebook • Visual model builder (2.0 +) • Python/scikit learn/Spark 2.1.1 (IzODA)++ • Model Training on Linux++ Deploy • Single click deployment • Multi-model version support+ • Batch deployment++ Live System • Spark/MLEAP online scoring* • PMML model online scoring* • Runtime performance monitoring • HA Scoring Service++ • Evaluation/Feedback for PMML models++ Data Scientists & Researchers Machine Learning & Production Engineers Data Engineers Machine Learning & Production Engineers • Data connector+ • Feedback data ingestion • Continuous model performance monitoring • On-demand retraining+ The Machine Learning Workflow • July release+ • June release* • Admin dashboard • Collaboration in project+ • Role based user access control+ • Sept release++
  6. 6. © 2017 IBM Corporation6 Machine Learning for z/OS Roadmap - 2017 April 2017 June 2017 July 2017 • Download & Go common installer • Jupyter on z support • Support for application cluster on Linux on z • Consistent User Interface experience across model creation and deployment/ management views • PMML scoring engine (SAS/R models conforming to PMML standards can be supported) • MLeap scoring engine • Feedback data ingestion directly to Db2 for z/OS • Administration Dashboard for resource management directly from Web User Interface • On demand retraining • Multi-version support • Model governance phase 1 – user and model access control • Visual model builder 2.0 • Support for additional transformers and algorithms • Auto data preparation Sep 2017 • Support for Python/scikit learn with IzODA • Model creation and training on x86 with deployment and scoring remaining on z/OS • Train with Scala/Python/R on x86 • Batch deployment • Monitoring and feedback data ingestion for PMML model • Zero downtime for scoring service 4Q 2017 • Model Templates – ITOA use case sample • Online scoring service in CICS integrated Liberty • Remote Spark cluster Support • Sklearn model feedback evaluation • Sandbox for deployment and scoring • Automatic retraining / redeployment • Complete services in application cluster installed on Linux on Z • Canvas for Spark • ODM (Operational Decision Manager) integration • Model governance – audit trace • Automatic path in visual model builder • Security enhancement for IzODA • 3rd party authentication product with ML for z/OS • Installation simplification by zOSMF workflow