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IBM Event: Making Data Ready for AI


Daniel Hernandez kicked things off with a discussion of data for AI. AI adoption, IBM says, is accelerating with 94% of companies believing it is important but only 5% are adopting aggressively. To address perceived issues, IBM introduced its ladder to AI

  • Trust
  • based on Automate (ML)
  • based on Analyze (scale insights)
  • based on Organize (trusted foundation)
  • based on Collect (make data simple and accessible)

This implies you need a comprehensive data management strategy that captures all your data, in rest and in motion, in a cloud-like way (COLLECT). Then it requires a data catalog so the data can be understood and relied on (ORGANIZE). Analyzing this data requires an end to end stack for machine learning, data science and AI (ANALYZE). IBM Cloud Private for Data is designed to deliver these capabilities virtually everywhere and embeds the various analytic and AI runtimes. This frames the R&D work IBM is doing and where there expect to deliver new capabilities. Specifically:

  • New free trial version available at a very long URL I can’t type quickly enough. This lets you try it.
  • Data Virtualization to allow users to query the entire enterprise (in a secure, fast, simple way) as though it was a single database.
  • Deployable on Red Hat OpenShift with a commitment to certify the whole stack on the Red Hat PaaS/IaaS.
  • The partnership with Hortonworks has been extended to bring Hadoop to Docker/Kubernetes on Red Hat.
  • Working with Stackoverflow to support ai.stackexchange.com

A demo of ICP for Data in the context of a preventative maintenance followed. Key points of note:

  • All browser based of course
  • UI is structured around the steps in the ladder
  • Auto discovery process ingests metadata and uses AI to discover characteristics. Can also crowdsource additional metadata
  • Search is key metaphor and crosses all the sources defined
  • Supports a rich set of visualization tools
  • Data science capabilities is focused on supporting open source frameworks – also includes IBM Research work
  • All models are managed across dev, staging and production and support rolling updates/one-click deployment
  • CPLEX integrated into the platform also for optimization