Software Solutions

Whether pre-installed or downloaded from the cloud, deep learning frameworks are key to executing deep learning – however many of the associated data science tasks can be time consuming and impede progress. For example prior to deep learning, any data must be verified, maybe resized and labelled – it may also be required to transfer the data between programs to do this. Recognising this, Scan has teamed up with a number of partners to offer solutions that automate many of these processes, or deliver visual insight into what the data you have means. Adding software like these into your ecosystem not only reduces time to results, but minimises reliance on expensive data scientist resource.

H2O AI Logo

H2Oai’s Driverless AI platform, is now fully integrated on the NVIDIA DGX systems, and is available as part of your proof-of-concept with Scan. It allows business users, analysts and data scientists use an incredibly fast, intuitive, integrated computing platform. Customers can apply Automatic Feature Engineering and quickly develop hundreds of machine learning models to help your business mitigate risks and maximise revenue potential.

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SQream Logo

SQream DB uses advanced, patented algorithms to give you fast access to your data. The GPU plays an important role in helping realize this. By offloading computationally-intensive operations to the GPU, SQream DB makes ingestion and analysis of data up to 100x faster, compared to industry-leading solutions. SQream DB's advanced raw data capabilities and SQL compatibility allow for immediate ad-hoc querying - a perfect match for data exploration, data discovery, and data science. With SQream DB's big data architecture, you don't need lengthy pre-aggregations, cubes, indexing or remodelling every time your query changes.

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Kinetica Logo

Kinetica’s distributed, in-memory database on NVIDIA DGX-1 and NVIDIA GPUs provides real-time analytics on data in motion and at rest 10-100x faster performance at 1/10 of the cost of traditional systems. NVIDIA and Kinetica together deliver unmatched performance, predictable scalability across multiple high-density nodes, and seamless integration with industry-standard connectors to data sources and applications. Kinetica’s User Defined Functions (UDFs) further deliver the first converged AI and BI workloads accelerated by NVIDIA GPUs.

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OmniSci Logo

GPU-accelerated analytics applications are now available in the NVIDIA DGX container registry and NVIDIA GPU Cloud (NGC). These applications, including MapD, provide customers the ability to abstract insights in milliseconds, build models with transparency and accuracy, and eliminate any integration complexity. They are tested and deployed on DGX systems and supported NGC platforms and are available to use immediately as part of your Scan proof-of-concept trial.

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Planet Logo

Robovision offers a fast track in AI to get added value from your data. Some years ago, this intelligence had to be created by humans writing rules to segment through the data. Now with the advent of deep learning supercomputers, such as the NVIDIA DGX-2, this intelligence can be created by deep learning structures.

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Planet Logo

Planet AI is a team of scientists and engineers with deep roots in Artificial Intelligence, Machine Learning and Cognitive Computing undertaking its own ambitious research projects towards Deep Universal Sequence Understanding. These skills are accomplished with many experiences in the domains of image and signal processing and has resulted in winning competitions over three years in the field of handwriting recognition, keyword spotting and document analysis.

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Clusterone Logo

Clusterone is a deep learning platform that makes it simple and fast to run deep learning workloads of any scale and complexity on any infrastructure. Clusterone helps organisations maximise the value of their data scientists by shielding them from the time sink of infrastructure management, and reduces project costs by maximising the efficiency of resource allocation and management. These efficiency gains result in an increased capacity to perform deep learning experiments and translate to faster research cycles and outputs.

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