Fabric for Deep Learning (FfDL)
Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.
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IBM SPSS Statistics
IBM SPSS Statistics software is used by a variety of customers to solve industry-specific business issues to drive quality decision-making.
Advanced statistical procedures and visualization can provide a robust, user friendly and an integrated platform to understand your data and solve complex business and research problems.
• Addresses all facets of the analytical process from data preparation and management to analysis and reporting
• Provides tailored functionality and customizable interfaces for different skill levels and functional responsibilities
• Delivers graphs and presentation-ready reports to easily communicate results
Organizations of all types have relied on proven IBM SPSS Statistics technology to increase revenue, outmaneuver competitors, conduct research, and data driven decision-making.
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DeepCube
DeepCube focuses on the research and development of deep learning technologies that result in improved real-world deployment of AI systems. The company’s numerous patented innovations include methods for faster and more accurate training of deep learning models and drastically improved inference performance. DeepCube’s proprietary framework can be deployed on top of any existing hardware in both datacenters and edge devices, resulting in over 10x speed improvement and memory reduction. DeepCube provides the only technology that allows efficient deployment of deep learning models on intelligent edge devices. After the deep learning training phase, the resulting model typically requires huge amounts of processing and consumes lots of memory. Due to the significant amount of memory and processing requirements, today’s deep learning deployments are limited mostly to the cloud.
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Adverity
Adverity is the fully-integrated data platform for automating the connectivity, transformation, governance and utilization of data at scale.
The platform enables businesses to blend disparate datasets such as sales, finance, marketing, and advertising, to create a single source of truth over business performance. Through automated connectivity to hundreds of data sources and destinations, unrivaled data transformation options, and powerful data governance features, Adverity is the easiest way to get your data how you want it, where you want it, and when you need it.
Adverity was founded in 2015 and is headquartered in Vienna with offices in London and New York, and currently works with leading brands and agencies including Unilever, Bosch, IKEA, Forbes, GroupM, Publicis, and Dentsu.
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