New service significantly accelerates developing and scaling machine learning models while eliminating infrastructure complexities
Arrikto, the leader in machine learning on Kubernetes, today announced the availability of their Kubeflow as a Service offering, an on-demand MLOps platform that turns complex machine learning workloads on Kubernetes into a point-and-click operation. With Arrikto’s Kubeflow as a Service, data scientists can now gain access to a complete MLOps platform, in just minutes without having to have any special knowledge of the underlying infrastructure.
The majority of machine learning projects don’t end up delivering on their promised ROI, because they tend to fail before they even make it to production. Machine learning workflows require specialized, technical skills that take significant time for an organization to build or hire for. Additionally, complex software is often needed to manage the underlying infrastructure, data, model training, hyperparameter tuning, metadata tracking, serving and security needs of the workload. As a result, data scientists are often asked to become DevOps experts in order to move their models into production, and vice versa.
To help close these skills and infrastructure management gaps, Arrikto’s Kubeflow as a Service abstracts away the complexity of running an MLOps platform on Kubernetes. It allows both data scientists and DevOps teams to work from a common toolset, so organizations can reduce their development times, streamline technical collaboration, and harness the power of Kubernetes to scale their models from the local laptop to a global GPU-powered cluster, all while avoiding vendor or service lock-in.
“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,” said Constantinos Venetsanopoulos, CEO at Arrikto. “When an organization deploys Kubeflow in production – whether on-prem or in the cloud – Arrikto’s Kubeflow as a Service will turbocharge the process.”
Kubeflow is an open source MLOps platform, originally developed by Google that includes integrated components for model training, multi-step pipelines, AutoML, serving, monitoring and artifact tracking.
Arrikto’s Kubeflow as a Service makes starting and scaling projects with Kubeflow easy so that organizations can realize the potential of their ML projects. Kubeflow as a Service comes with a free 14-day trial that makes it easy for both data scientists and DevOps engineers to evaluate Kubeflow as a potential MLOps platform for their organization. Sign up to get started.
Visit AITechPark for cutting-edge Tech Trends around AI, ML, Cybersecurity, along with AITech News, and timely updates from industry professionals!
