Fog Computing Group Publishes Reference Architecture

#artificialintelligence

The OpenFog Consortium released its OpenFog Reference Architecture. And in case you're wondering what in the heck OpenFog is -- which sounds a bit like an oxymoron -- it's a group whose members are working on "fog computing," which adds a hierarchy of compute, storage, networking, and control functions between the cloud and endpoint devices and between gateways and devices. The OpenFog Reference Architecture creates fog computing standards to enable the data-intensive requirements of the Internet of Things (IoT), 5G, and artificial intelligence (AI) applications. The OpenFog Consortium was founded over one year ago, and it's an independent nonprofit organization run under the direction of its board of directors. Its committees and workgroups are run by its members.


Big Data Reference Architecture Standards

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The ISO/IEC DIS 20547-3.2 has passed ballot (INFORMATION TECHNOLOGY -- BIG DATA REFERENCE ARCHITECTURE -- PART 3: REFERENCE ARCHITECTURE) In plain language Working Group 2 of the International Standards Organisation (ISO) and International Electrotechnical Council (IEC) Joint Technical Committee's (JTC1) Standards Committee 42 (SC42) has been working on developing the global standard Big Data Reference Architecture (BDRA) for the last few years and now it has passed another ballot of 27 Countries to reach Draft International Standard (DIS) stage. Next steps will be taken at the Plenary of the SC42 Artificial Intelligence committee meeting in Tokyo starting October 7th to October 11th 2019. There we hope to complete the work on the BDRA Reference Architecture to submit for Final Draft International Standard then publish. I'd like to commend the work of Dave Boyd, Liang Guang, Suwook Ha, Toshihiro Suzuki and our convenor Wo Chang for steering this document to this milestone. See you all soon in Tokyo!!


IIC: Industrial IoT Reference Architecture

@machinelearnbot

One can find many ROI opportunities when looking at IoT to solve problems and bring value in an industrial context. Process improvement, asset tracking, and preventative maintenance are the three major pillars that drive ROI opportunities. The constant churning wheel of New Industrial IoT Platforms from vendors new and old can be staggering for those working to specify overall systems that address these opportunities. Whether it be Bosch or Cisco, GE or Hitachi, Honeywell or Rockwell Automation, the various offerings stagger in their feature sets, and force the customer to speak different languages. This is unfair to the to the specifier, as the definitions, structures, and points of views all vary greatly.


OpenFog publishes reference architecture for fog computing

#artificialintelligence

The OpenFog Consortium announces the release of the OpenFog Reference Architecture, a universal technical framework designed to enable the data-intensive requirements of the Internet of Things (IoT), 5G and artificial intelligence (AI) applications. The RA marks a significant first step toward creating the standards necessary to enable high-performance, interoperability and security in complex digital transactions. Fog computing is the system-level architecture that brings computing, storage, control, and networking functions closer to the data-producing sources along the cloud-to-thing continuum. Applicable across industry sectors, fog computing effectively addresses issues related to security, cognition, agility, latency and efficiency. The OpenFog Consortium was founded over one year ago to accelerate adoption of fog computing through an open, interoperable architecture.


NEW AZURE REFERENCE ARCHITECTURE: Real-time scoring of R machine learning models – AzureCAT Guidance

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We have a new AI Reference Architecture (on the Azure Architecture Center) from AzureCAT Data Scientist, Hong Ooi. It was edited by Nanette Ray and Mike Wasson. It was reviewed by George Iordanescu (also from AzureCAT AI). This reference architecture shows how to implement a real-time (synchronous) prediction service in R using Microsoft Machine Learning Server running in Azure Kubernetes Service (AKS). This architecture is intended to be generic and suited for any predictive model built in R that you want to deploy as a real-time service.