Brief description: 

The key concept proposed in the DEEP Hybrid DataCloud project is the need to support intensive computing techniques that require specialized HPC hardware, like GPUs or low-latency interconnects, to explore very large datasets. A Hybrid Cloud approach enables access to such resources that are not easily reachable by the researchers at the scale needed in the current EU e-infrastructure.

We also propose to deploy under the common label of “DEEP as a Service” a set of building blocks that enable the easy development of applications requiring these techniques: deep learning using neural networks, parallel post-processing of very large data, and analysis of massive online data streams. These services will be deployed in the project testbed, offered to the research communities linked to the project through pilot applications, and integrated under the EOSC framework, where they can be further scaled up in the future.

DEEP-Hybrid-DataCloud is delivering a comprehensive platform to easily develop, build, share and deploy Artificial Intelligence, Machine Learning and Deep Learning modules on top of distributed e-Infrastructures.

In the DEEP Open Catalog, you can find ready to use modules in a variety of domains. These modules can be executed on your local laptop, on a production server or on top of computing e-Infrastructures supporting the DEEP-Hybrid-DataCloud stack.

A series of training videos and documentation has been released to facilitate the use of DEEP Open Catalog modules by early-adopters. 

Main Features: 

A comprehensive platform for machine learning, deep learning, and artificial intelligence in the European Open Science Cloud. Developing, training, sharing, and deploying your model has never been easier, click here!

The DEEP training facility, accessible through the DEEP training dashboard allows data scientists to develop and train their models, with access to the latest generation EU computing e-Infrastructures.

DEEP as a Service is a fully managed service that allows to easily and automatically deploy the developed applications as services, with horizontal scalability thanks to a serverless approach. The pre-trained applications that are published in the catalog are automatically deployed as services to make them available for general use.

DEEP Open Catalog and marketplace comprises a curated set of applications ready to use or extend, fostering knowledge exchange and re-usability of applications. This open exchange aims to serve as a central knowledge hub 5 for machine learning applications that leverage the DEEP-Hybrid-DataCloud stack, breaking knowledge barriers across distributed teams. Moreover, pre-configured Docker containers, repository templates and other related components and tools are also part of this catalog.

DEPPaaS API enables data scientists to expose their applications through an HTTP endpoint, delivering a common interface for machine learning, deep learning and artificial intelligence applications.

The DEEP training dashboard allows to easily train any existing modules or your own one.

Areas of Application: 
  • AI, Machine Learning and Deep Learning
  • DEEP Open catalog provides models related to Earth Observation, Biological and medical science, cyber-security and networking monitoring and general science
  • For COVID-19: Genetic studies, X-ray image classification, Data science to understand confinement effectiveness 
Market Trends and Opportunities: 

Machine Learning-as-a-service delivers efficient lifecycle management of machine learning models and it is one of the fastest-growing services in the cloud.

DEEPaaS provides Machine/Deep Learning as a service to offer an easy integration path to the developers of AI Applications.

DEEPaaS can be used in all markets with AI application needs, such as Health, Industry 4.0, Smart cities, etc. 

Customer Benefits: 

•Ease and lower the entry barrier for non-skilled scientists to the use of intensive computing services

•Integrate intensive computing services under a hybrid cloud approach

•Transparent execution on different e-Infrastructures

•Implement common software development techniques also for scientist’s applications (DevOps)

•Low learning curve for the developers of the solutions

Technological novelty: 

•This solution provides added value services that span various sites (hybrid) to scientists to ease access to computing power without technology knowledge

•Develop catalogs or marketplaces with ready-to-use solutions

•Ensure interoperability with the existing EOSC platforms and their services

•Ready to use applications, ready to use APIs, ready to use clusters

•Possibility to redeploy the whole stack and architecture used for a research

•Offer a DevOps approach for the application development

Platform / Framework


BDV Reference categories: 
Data Analytics
Data Management
Data processing architectures
Agriculture, forestry and fishing
Information Service activities
Health and Wellness
Urban Mobility
Readiness Level: 
Machine Learning
Deep Learning