DKube is a portable, end-to-end, Kubeflow-based MLOps platform that enables data scientists to develop, tune, and deploy complex models. It is based on Kubernetes, and will run on-premises and on the most popular cloud providers.It has the same look, feel, and workflow on all of them, and migrating back and forth between providers is fast and simple.
The next generation of enterprise applications will increasingly be AI/ML models applied to accelerate existing processes or solve new problems such as accelerating drug discovery and development in life sciences. Kubeflow is an open source reference architecture for AI/ML platform initiated by Google
Democratization of AI is a popular topic these days, and rightly so. It is necessary for mass adoption and enablement in many industries in order to solve their specific problems using AI — industries such as manufacturing, healthcare, and automotive all benefit from this approach.
What It can Learn from the Industrialization of Supermarkets.
Relying on Github or similar methods for code and data repos alone with Tensorflow, PyTorch or similar frameworks is not sufficient to scale AI projects and deployments. Demand more from your AI platform vendor. Demand MLOps.
One Convergence CEO Prasad Vellanki sat down to discuss the obstacles and promise of Deep Learning at the TF World 2019 show. Prasad offers a compelling vision of where the industry is headed, and explains how the company’s DKube product offers a powerful, flexible, and affordable Deep Learning solution for on-prem, cloud, and hybrid platforms.
Cloud-based computing has changed the face of IT for the better. It has enabled organizations to make use of high-performance resources without requiring large IT groups. And it has enabled a supply of production-ready applications to companies who might not otherwise be able to access them. This is especially true in the world of Deep Learning, where high performance, scalability, and flexibility are critical. But what if your organization can’t...
The Duchess of Windsor famously said that you could not be too rich or too thin. And whether or not that is correct, a similar observation is definitely true when trying to match deep learning applications and compute resources: you cannot have enough horsepower. Intractable problems in fields as diverse as finance, security, medical research, resource exploration, self-driving vehicles, and defense are being solved today by “training” a complex neural...