As one of the leading multi-GPU deep learning cloud providers, we have a lot of partners that are working to further the advancements of deep learning. We wanted to build a community where we could help our customers connect with our partners to save time, money, and resources. The partners included in our AI Ecosystem have tested their products on our platform and have worked special arrangements with us to provide their solutions to you through our cloud service. They've all agreed to offer their services as a flat-rate included with our service, or as a stand-alone product, so you can continue to trust that you won't ever get a variable bill from us.
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Its goal is to make scaling machine learning (ML) models and deploying them to production as simple as possible, by letting Kubernetes do what it’s great at: (1) Easy, repeatable, portable deployments on a diverse infrastructure, (2) Deploying and managing loosely-coupled microservices, (3) Scaling based on demand. Anywhere you are running Kubernetes, you should be able to run Kubeflow.
The Lucd Artificial Intelligence platform is a highly flexible platform that includes applications for data ingestion, exploration and business intelligence. In addition, Lucd is a platform for rapidly creating business specific AI capabilities and solutions.
With DKube from One Convergence, the data scientists do not need extensive IT expertise to find, download, and maintain ML frameworks, CUDA libraries, RDMA tools and various of other tools and libraries. Dkube installs on the Cirrascale Cloud Services platform in less than hour and can onboard a customer's data scientists quickly allowing them to conduct their Tensorflow or PyTorch experiments with ease.
Spell lets you start running cloud ML projects out of the box so you can get to the fun stuff faster. Start-ups and big businesses alike use Spell to accelerate discovery and manage their end-to-end machine learning pipeline. Easily keep track of models and results, and deploy them to Kubernetes/Kubeflow with one click.
H2O is a fully open source, distributed in-memory ML platform with linear scalability. H2O supports the most widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. It has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models.
The OmniSci platform is designed to overcome the scalability and performance limitations of legacy analytics tools faced with the scale, velocity and location attributes of today’s big datasets. Those tools are collapsing, becoming too slow and too hardware-intensive to be effective in big data analytics. OmniSci is a breakthrough technology, originating from MIT, designed to leverage the massively parallel processing of GPUs alongside traditional CPU compute, for extraordinary performance at scale.
PowerAI Vision makes computer vision with deep learning more accessible to business users. PowerAI Vision includes an intuitive toolset that empowers subject matter experts to label, train, and deploy deep learning vision models, without coding or deep learning expertise. It includes the most popular deep learning frameworks and their dependencies, and it is built for easy and rapid deployment and increased team productivity. By combining PowerAI Vision software with accelerated IBM® Power Systems™, enterprises can rapidly deploy a fully optimized and supported platform with blazing performance.
The absolute fastest storage offering to remove bottlenecks faced by customers who use inference training datasets consisting of millions of files to improve outcomes and increase accuracy of deep learning models. WekaIO’s Matrix software is a fully parallel and distributed file system that has been designed from scratch to leverage Flash technology. Both data and metadata are distributed across the entire storage infrastructure to ensure massively parallel access to NVMe drives.