As more companies move to the cloud, they are seeking ways to use rapidly growing technologies such as Kubernetes, and the benefits they offer, in various and new ways, tailored to their specific data needs and customized to what works best for certain workloads. On IBM Cloud, Kubernetes can now fit into an organization’s cloud strategy no matter what that looks like; whether it’s building a completely cloud-native machine learning app, accessing servers directly to handle large data workloads or migrating data-heavy apps to the cloud.

IBM has also recognized the potential that containers hold for new and data-intensive workloads. Many of these, such as machine learning apps, require high levels of computing power that bare metal excels at delivering. Until now, running containers on bare metal required considerable configuration and constant management from developer teams. This limited their use on bare metal with complex apps in production, where the benefits of a managed service, such as automatic updating, intelligent scaling and built-in security, prevail.

This is why the IBM Cloud Container Service, a fully managed container service based in Kubernetes, can now run on bare metal nodes. This gives developers greater control over where their workloads reside and enables them to isolate workloads to specific servers. It equips teams with all of the benefits of a fully managed container service combined with the performance and security of bare metal.

Working with the open community, IBM is also driving the availability of apps built with Kubernetes to directly access GPUs. GPU support is a make-or-break component of many cloud-enabled technologies, such as machine learning, which are very data and compute-intensive and need a higher-than-usual level of performance to be competitive.

Working in the IBM Cloud Container Service, developers can now choose bare metal machine configurations that meet their needs, whether that’s isolation, increased processing capability, or large local disk storage, while taking advantage of the benefits of containers, such as the ability to move data easily across systems or to allow multiple team members to work on multiple parts of an app simultaneously.

This marks the next evolution in the IBM investment in containers and to our commitment to grow them as a secure, stable and widely adopted component of companies’ cloud strategies. IBM has been an early contributor to Kubernetes since it was brought to the open community and has helped to lead many projects in the open community that advance the security and scale of containers. This includes our work with Google and Lyft to build Istio, which helps to secure and orchestrate microservices, as well as our collaboration with Google to launch Grafaes, which helps secure the supply chain of code around containers as they’re deployed and used.

Evolving our own container service to the meet the demands of high-powered workloads is another step in advancing the opportunity of containers, as well as designing our cloud for data. Bringing Kubernetes to bare metal will help organizations extract more value out of their data, apps and workloads, and better fuel innovation in areas such as machine learning, which will define the next generation of technology.

Learn more about the IBM Cloud Container Service.