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AI Research Junior is your always-on AI research partner — built to supercharge discovery with speed, precision, and full-stack security. Designed for biotech and healthcare, it turns complex research into a faster, smarter, and more scalable process.

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.

Machine 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 to a mainstream discipline. Until recently, only experts could navigate the complex patchwork of tools and processes required to create such a system.

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.

Over the past two plus years Kubeflow has come together a long way in bringing together many open source innovations to implement a cost effective AI/ML platform. These include KF Pipelines, KF Serving, AutoML, Istio from Kubernetes in addition to framework operators like Tensorflow, PyTorch, XGBoost, SciKit. However, “implementing” Kubeflow with your preferred cloud or on-prem environment still requires significant work still, with few people, for many months. The work comes in terms of:

Reproducibility is the foundational principle of the scientific method. If an experiment cannot be repeated, it is assumed to be faulty. DKube, an end-to-end Kubeflow-based MLOps platform, offers complete reproducibility into an integrated workflow. Without the ability to trace and repeat your work, it is not science