The leading provider of machine learning (ML) on Kubernetes, Arrikto, has launched its Kubeflow as a Service offering. This on-demand MLOps platform simplifies the deployment of complicated machine learning workloads on Kubernetes. Data scientists can now quickly and easily access a complete MLOps platform with Arrikto’s Kubeflow as a Service without needing to be experts in the underlying infrastructure.

“Kubeflow as a Service gives both data scientists and DevOps engineers the easiest way to use an MLOps platform on Kubernetes without having to request any infrastructure from their IT departments. When an organization deploys Kubeflow in production – whether on-prem or in the cloud – Arrikto’s Kubeflow as a Service will turbocharge the process,” stated Constantinos Venetsanopoulos, CEO at Arrikto.

Due to their propensity to fail before being put into production, most machine learning programs don’t achieve the ROI they intended. It takes a lot of time for an organization to develop or hire the specific technical capabilities needed for machine learning operations. The workload’s underlying infrastructure, data, model training, hyperparameter tweaking, metadata tracking, serving, and security needs require complex software. As a result, in order to put their models into production, data scientists are frequently requested to become specialists in DevOps and vice versa.

The difficulty of managing an MLOps platform on Kubernetes is eliminated by Arrikto’s Kubeflow as a Service to aid in bridging these skills and infrastructure management gaps. Organizations can shorten their development cycles, enhance technical cooperation, and use Kubernetes to scale their models from a local laptop to a global GPU-powered cluster while avoiding vendor or service lock-in by enabling both data scientists and DevOps teams to work from a single toolbox.