IBM takes the next step with Cloud Pak for Data


Ever since IBM unveiled Cloud Pak for Data as an integrated cloud suite of analytics and AI platform, we have wondered when IBM would take the next step and announce a fully managed cloud service. It’s starting to happen now as IBM is rolling out the IBM Cloud Pak for Data as a Service.

Roll the tape back until last spring when we went through IBM Cloud Satellite; we noticed that IBM’s primary cloud message was about multi-cloud or at least cloud agnostic. Powered by Red Hat OpenShift, IBM created such a strategy for this managed Kubernetes environment, where you could implement open source software on the selected hardware or public cloud or choose IBM to run a managed OpenShift service for you in IBM Cloud. It is now being repeated with Cloud Pak for Data.

To summarize, Cloud Pak for Data consists of an integrated set of services for building analysis and machine learning models. Unusual among hybrid cloud offerings, Cloud Pak for Data also has a third-party ecosystem of supported services. Among them are MongoDB and Cockroach DB – these are partner services that are supported on the platform, but which are purchased by the customer via BYOL and installed separately. It is part of a broader portfolio of integrated packages of prepackaging tools to create your own PaaS environments. In addition to Cloud Pak for Data, there are other Cloud Paks development of cloud-native applications; API integration; automation online workflows; multi-cloud management; and security.

The platform has been available in several form factors. You can implement it on any cloud as a software package (Cloud Pak for Data) or on a hyperconverged system (Cloud Pak for Data System). What we wanted at Cloud Pak for Data was the underlying technique of mixing these solutions, so unlike most IBM software, the burden of integrating does not lie with the customer or system integrator. The basic package includes the database (Db2 storage in most editions); data discovery (Watson Knowledge catalog); ML model life cycle management (Watson Studio); and AI Performance Tracking (Explainable AI (formerly Watson OpenScale). In addition to these building blocks, there are nearly 30 services from IBM, third-party partners, and open source technologies that can be integrated into the Cloud Pak for Data platform. Additional services are priced, ordered, and in some cases installed separately.

Nevertheless, the platform is remarkable for IBM in providing a cohesive end-to-end environment to get machine learning and analysis started; for the basic components you do not need to install or integrate software. In addition to the appeal, workflows in the software are very intuitive and suitable for self-service. The catch was that until now you had to install software or buy a turnkey system (Cloud Pak for Data System) to get it started. So now the other shoe has fallen and IBM is ready to offer Cloud Pak for Data as a managed service on IBM Cloud, making it a true self-service platform.

The first launch of the managed service in IBM Cloud back in the summer was not announced. IBM is taking a rolling thunder approach to the managed Cloud Pak for Data service and is gradually bringing more data and AI services to the platform.

The basic pieces are in place, but the data integration components, including DataStage technologies for ETL, data replication and data virtualization are on the horizon in early mid-2021. The same goes for Cognos Analytics. Most of the partner services do not yet have to jump to the managed service. Coming out of the gate, there are several basic Watson services, including chatbot; capabilities to add industry-specific domain knowledge; language translation and speech to text (and vice versa). Some of the more esoteric options (e.g. Natural language processing, personality insight, image recognition) are now available.

As mentioned, Cloud Pak for Data provides a good example of design thinking, where the software, user interface and workflows are well out. We were waiting for IBM to finally take the plunge and offer this as a managed shadow service and finally make it the self-service platform for which it was conceived.