BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Michigan BEGIN:DAYLIGHT DTSTART:20170312T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20161106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20170211T164930Z UID:1DD9A49B-E53F-11E6-A7C6-0050568D7F66 DTSTART;TZID=US/Michigan:20170215T180000 DTEND;TZID=US/Michigan:20170215T193000 DESCRIPTION:Vehicle Development platforms for new automotive products will change substantially over the next few years. Traditional platforms are hi ghly limited in areas of incorporating machine learning\, AI development\, IOT integration\, V-I public cloud integration\, AI-based security system s to protect the product development ecosystem\, etc. Most existing automo tive platforms are best-of-breed\, integrated stacks that are designed fro m traditional IT products and services. Much of the productivity and effic iency improvements that have been implemented by major cloud providers are not realized in these vehicle development platforms.\n\nDuring the hour\, considerations and recommendations will be presented that build upon conf igurable platform elements that manage different platform capabilities - I OT/sensors\, machine learning\, computing methods (traditional CPU\, graph ic processor cards\, FPGAs\, and ASICs)\, key technology standards\, and a profile of skills that will be necessary to develop and operate these nex t generation platforms. These platforms will also incorporate new software capabilities emerging in the cloud that can dramatically improve automoti ve product quality\, speed and productivity requirements. Examples include Amazon Glue (collection of Python scripts that automatically crawls IOT d ata sources and apply data organization and transforms before storing)\, A mazon Alexa (voice recognition & control) adapted to automotive\, Google ’s TensorFlow (software and hardware to improve machine learning/AI)\, A mazon EC2 P2 Chips to create/train deep neural networks for on-demand infr astructure\, functional to functional compute engines (e.g.\, serverless c omputing)\, Amazon MongoDB Atlas (distribute replicas across multiple avai lability zones (e.g.\, 3\, 5\, 7)\, and Facebook’s OSquery (provide real time\, reliable visibility into systems running throughout your network t o quickly identify and investigate anomalies). Challenges related to creat ing/managing these platforms will also be presented. Finally\, considerati ons that identify which platform elements should be available in the vehic le\, in the cloud\, and in-both will be presented.\n\nCo-sponsored by: Sub ramaniam Ganesan\n\nSpeaker(s): Bill Bone\, \, Bill Bone\, \n\nAgenda: \n6 :00 PM - Welcome and Introductions\, Chapter business update\n\n6:15 PM - Technical Talk\n\n7:15 PM - Q & A\n\n7:30 PM - Wrap Up\n\n7:30 to close - Networking\n\nRoom: EC 116\, Bldg: Engineering Center\, EC 116\, Engineeri ng Center\, Oakland University\, Rochester\, Michigan\, United States\, 48 309-4479 LOCATION:Room: EC 116\, Bldg: Engineering Center\, EC 116\, Engineering Cen ter\, Oakland University\, Rochester\, Michigan\, United States\, 48309-44 79 ORGANIZER:sharan.kalwani@ieee.org SEQUENCE:6 SUMMARY:Technical Meeting: Cloud Platform Considerations for Automotive Pro duct Development URL;VALUE=URI:https://events.vtools.ieee.org/m/43458 X-ALT-DESC:Description: <br /><p>Vehicle Development platforms for new auto motive products will change substantially over the next few years.&nbsp\; Traditional platforms are highly limited in areas of incorporating machine learning\, AI development\, IOT integration\, V-I public cloud integratio n\, AI-based security systems to protect the product development ecosystem \, etc.&nbsp\; Most existing automotive platforms are best-of-breed\, inte grated stacks that are designed from traditional IT products and services. &nbsp\; Much of the productivity and efficiency improvements that have bee n implemented by major cloud providers are not realized in these vehicle d evelopment platforms.</p>\n<p>During the hour\, considerations and recomme ndations will be presented that build upon configurable platform elements that manage different platform capabilities - IOT/sensors\, machine learni ng\, computing methods (traditional CPU\, graphic processor cards\, FPGAs\ , and ASICs)\, key technology standards\, and a profile of skills that wil l be necessary to develop and operate these next generation platforms.&nbs p\; These platforms will also incorporate new software capabilities emergi ng in the cloud that can dramatically improve automotive product quality\, speed and productivity requirements. Examples include Amazon Glue (collec tion of Python scripts that automatically crawls IOT data sources and appl y data organization and transforms before storing)\, Amazon Alexa (voice r ecognition &amp\; control) adapted to automotive\, Google&rsquo\;s TensorF low (software and hardware to improve machine learning/AI)\, Amazon EC2 P2 Chips to create/train deep neural networks for on-demand infrastructure\, &nbsp\; functional to functional compute engines (e.g.\, serverless comput ing)\, Amazon MongoDB Atlas (distribute replicas across multiple availabil ity zones (e.g.\, 3\, 5\, 7)\, and Facebook&rsquo\;s OSquery (provide real time\, reliable visibility into systems running throughout your network t o quickly identify and investigate anomalies). Challenges related to creat ing/managing these platforms will also be presented. Finally\, considerati ons that identify which platform elements should be available in the vehic le\, in the cloud\, and in-both will be presented.</p><br /><br />Agenda: <br /><p>6:00 PM - Welcome and Introductions\, Chapter business update</p> \n<p>6:15 PM - Technical Talk</p>\n<p>7:15 PM - Q &amp\; A</p>\n<p>7:30 PM - Wrap Up</p>\n<p>7:30 to close - Networking&nbsp\;</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR