achine Learning offers the promise of revolutionizing AI. But to achieve that promise, it needs to move from something that only a few experts can use
Over the last decade enterprises have made heavy investments in High Performance Computing (HPC) to solve complex scientific problems.
Kubeflow is rapidly becoming the most widely used open source reference architecture for teams wanting to build and support their own AI and ML platforms.
Reproducibility is the foundational principle of the scientific method. If an experiment cannot be repeated, it is assumed
Deploying Kubeflow on AWS, GCP, Azure of on-prem: It is a lot more than just downloading and installing open source
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
The next generation of enterprise applications will
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
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.
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.
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:
