IBM Cloud Pak for Data 2.5: Bringing open source to the core
It’s been an exciting time for IBM. We recently made the biggest software acquisition in history. Very rarely have I seen any big organization move so quickly and decisively to embrace open source and build a prescriptive methodology to modernize IT workloads. A key part of this strategy is Cloud Pak for Data, our modern Data and AI platform.
Today, we’re announcing the latest update to the IBM Cloud Pak for Data platform, version 2.5. We are extremely excited for this release, as it brings to a head three key areas we’ve been building for over the last year and a half: Red Hat integration, new key built-in capabilities and a heavy focus on open source.
Let’s start with Red Hat. Soon, Cloud Pak for Data will be fully integrated and certified on Red Hat OpenShift Container Platform, making it the architecture our platform is delivered on. We’ve been focused on the success of our developer community and the ability to easily infuse Cloud Pak for Data’s AI capabilities into Red Hat application development. We’re changing the game for our developer audience and making the goal of machine learning ops (MLOps) attainable.
We’ve already seen success with our hyper-converged infrastructure – IBM Cloud Pak for Data System – that includes Red Hat OpenShift. And with the core platform and ecosystem services now fully certified, we can showcase the potential of what’s possible when IBM and Red Hat join forces that will change how our customers embrace data and AI. For more, please check out this video on Cloud Pak for Data and Red Hat OpenShift.
Our foundation-building with Red Hat extends to the key capabilities of our end-to-end platform, which will be augmented greatly in the new release. As such, we’re now welcoming several new microservices into the base of Cloud Pak for Data: Watson Studio V2.0, Watson OpenScale, Watson Knowledge Catalog, Db2 Event Store, Infosphere Regulatory Accelerator and more, along with significant enhancements to IBM Data Virtualization.
Having these tools – and Watson in particular – available from install gives our customers a greater ability to build, manage and govern AI models. Perhaps the greatest one in the bunch is a new feature, AutoAI, which helps you build AI and automate the entire AI process. You can empower data scientists and enable power users to build, rank and deploy AI models in a few minutes, as opposed to weeks or months.
IBM Cloud Pak for Data is built to be open by design. We always strive to leverage open source where possible. In addition to the myriad of options currently available, including R and Python, we’ve now adding two new open source services: Analytics Engine for Apache Spark and Open Source Management. Apache Spark, a popular open source, distributed processing system commonly used for big data workloads, is now natively supported in Cloud Pak for Data. This service enables data scientists and application developers to run serverless Spark jobs with dedicated cluster, ensuring predictive and consistent performance while running complex algorithms and AI models.
The open source management service helps ensures governance of open source, a huge problem at many enterprise companies today. It can help you manage a curated set of open source packages, flag known security and vulnerability risks, help developers discover and collaborate on approved, open source packages and initiate approval requests for new open source adoption.
According to a recent article by Mckinsey, deployment of modern data architecture is a strategic differentiator and is more common among high performance companies to support their data and analytics at scale. That is exactly what we are working to enable with Cloud Pak for Data v2.5 with Red Hat OpenShift and a number of new capabilities makes it even more compelling.
Many other new details are contained within V2.5. All of these new benefits also carry over to our hyper-converged infrastructure, Cloud Pak for Data System. Please explore this website to learn more about Cloud Pak for Data.