IBM launches IBM AI OpenScale to combat AI sprawl as part of a broad open strategy - Techies Updates

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Thursday, October 18, 2018

IBM launches IBM AI OpenScale to combat AI sprawl as part of a broad open strategy

IBM AI OpenScale, Multi-cloud Manager and IBM Security Connect equate to a strategic push by Big Blue to be the agnostic integrator and platform provider for key enterprise technologies.


IBM launched IBM AI OpenScale, a platform designed to help enterprises build, run, manage and operate artificial intelligence applications.

The launch of AI OpenScale is part of IBM's ongoing effort to become a management plane for AI and add transparency to so-called black box approaches. If AI sprawl isn't here today it soon will be and enterprises are likely to have a management headache ahead. IBM has been pushing for more AI transparency and tools that allow data scientists, as well as business executives, find flaws in models.

For IBM, AI OpenScale is one front of a multi-pronged strategy to position its wares as being more open and serve as an integrator for data, multiple clouds, and security analysis. IBM's big message: Serving as an agnostic technology provider can better help enterprises.

Beth Smith, general manager of Watson Data and AI at IBM, said AI today is a mesh of tools, models, and frameworks. "People use a variety of tools. Some are rolling your own," said Smith, who added that IBM AI OpenScale is an interoperable system that can support AI implementations.

IBM's AI OpenScale platform, which will be available later this year on IBM Cloud and IBM Cloud Private, will operate AI applications and debug them for things like bias wherever they were built or currently run. The platform will support frameworks such as Watson, Tensorflow, Keras, SparkML, AWS SageMaker, Azure ML and others.

Part of the AI OpenScale launch includes NeuNetS, which is a system that can automate and build AI. Smith noted that NeuNetS can save data scientists time and can narrow an enterprise skills gap.

IBM AI OpenScale is designed to explain how AI applications reach decisions, provide an audit trail and ensure AI models are fair at runtime.

Here are some points and examples of how AI OpenScale would work in practice:


  • AI OpenScale manages and optimizes AI applications, but data scientists would build models in the framework of their choice.
  • However, IBM AI OpenScale would automate many items in the AI development process. For instance, de-biasing would be automated. "AI OpenScale would bring fairness to an attribute in a model and does it in a way that doesn't alter the base model," said Smith.
  • AI OpenScale would leave the original model alone but de-bias it with a new auto-generated model.
  • NeuNetS would be used to fine tune AI and models and could speed up development time by months. NeuNetS has been in use at IBM for "several months," said Smith, who said the service would start out as a beta within the platform.


For IBM AI OpenScale to work within an enterprise the client would have to be able to point Big Blue to a direct endpoint where the AI black box resides. IBM wouldn't be able to manage embedded AI in another application. For instance, Salesforce's Einstein couldn't be accessed from AI OpenScale, but the CRM giant could use IBM's platform to manage its models that it embeds into applications.

Separately, IBM launched Multi-cloud Manager, an operations platform based on Kubernetes containers to manage public and hybrid cloud deployments.

The console from IBM is optimized on IBM Cloud but can integrate and manage workloads on clouds from the likes of Amazon Web Services, Red Hat and Microsoft. The Multi-cloud Manager runs on IBM Cloud Private.

According to Big Blue, the differentiator for its cloud management console is that it's based on open standards to manage data and apps across clouds.

Multi-cloud Manager, available this month, includes:


  • The ability to interconnect different clouds, unify systems and automate operations.
  • A dashboard to manage thousands of Kubernetes applications and data where its located.
  • An integrated compliance and rules engine for enterprise policies and security standards.
  • Automation to define how, where and when Kubernetes applications are deployed and how they are backed up.
  • In addition, IBM outlined IBM Security Connect, an open platform that aims to aggregate and analyze federated security data across multiple systems and environments.

  • IBM Security Connect is based on machine learning and AI.


According to IBM, Security Connect will open its framework, micro-services, software developer kits and application programming interfaces for integration and development. IBM Security Connect will house the company's current Security App Exchange and all of its security applications.

The company also committed to using existing open security and protocol standards. IBM Security Connect will be available in the first quarter of 2019.


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