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The Extraordinary AI Blog

The world is changing, but not as rapidly as AI is.
Keep up with what's new, and what's relevant with DKube.

Blogs
From Weeks to Hours: Fast-Tracking Biotech Research with AI Research Junior

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.

Blogs
The Private AI Dilemma

Your data and LLM models reside in private cloud but GPU resources are spread across clouds.

Blogs
DKubeX GPU Cost Broker

There is a bidding war on GPU prices, so how do you find the resources you need within your project timelines? DKubeX can help.

Blogs
Securing Your Gen AI Applications

Protecting intellectual property in your corporate documents and LLM models, both inside and outside your organization.

Blogs
The Transformer Revolution: How it's Changing the Game for AI

While they hardly resemble the image of the shape-shifting robot that pops up in your mind, transformer models metaphorically do bear some resemblance to the transformers in the movies. These models are capable of transforming Natural Language Processing tasks in a way that no traditional machine learning algorithm could ever before. 

Blogs
An Efficient Scale-Out Deep Learning Cloud – Your Way

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.

Blogs
Scale-Out: The Right Way to Deploy On-Premises Deep Learning Lenovo™, NVIDIA®, Mellanox®, and One Convergence Deliver a Compelling Solution

Cloud-based computing 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. But, what if your organization can’t make use of the public cloud?

Blogs
Deep Learning MLOps with Prasad Vellanki, CEO of One Convergence

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.

Blogs
Kubeflow based MLOps platform comes to Nutanix On-Prem and Hybrid Cloud

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 and contributed by several IT platform infrastructure leaders in the industry such as IBM, Redhat, Cisco, Dell, AWS for on-prem and hybrid deployment of AI/ML.

Blogs
DKube™ : Kubeflow Implementation On-Prem or AWS,GCP,Azure

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:

Blogs
DKube™ Offers Integrated, Kubeflow-Based Reproducibility

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

Blogs
MLOps on HPC/Slurm with Kubeflow

Over the last decade enterprises have made heavy investments in High Performance Computing (HPC) to solve complex scientific problems. They have used Slurm to schedule these massively parallel jobs on large clusters of compute nodes with accelerated hardware. AI/ML uses similar hardware for deep learning model training and enterprises are looking to find solutions that provide AI/ML model development on top of their existing HPC infrastructure. A recent trend in AI/ML is to use agile MLOps methodologies to productionize AI/ML models quickly. Marrying the two - AI/ML development using MLOps with HPC/Slurm clusters - will lead to a much faster adoption of this combination. This article elaborates on how to combine popular open-source frameworks, Slurm and Kubeflow, to run AI/ML workloads at scale on HPC clusters.

Blogs
Create an Enterprise-Ready MLOps Platform Using Kubeflow

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.

Blogs
Keep Your Models Accurate Through Monitoring

Machine learning has been traditionally focused on the important initial training phase. This ensures that a model will provide the best outcomes for the expected live data stream - at least for a while. However, it is well known within the machine learning community that however good a job you do at training a model, your results will

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