ARIC

Work­shop: Machi­ne Lear­ning for Real-World App­li­ca­ti­ons (verschoben:tbd)

Mike Brandt and Jonas Wen­ke from 33A and Dr. Jor­ge Davi­la-Cha­con and Olaf Erich­sen from Hel­den­kom­bi­nat Tech­no­lo­gies GmbH.

Fre­quent­ly, com­pa­nies are pre­sen­ted with fla­shing Machi­ne Lear­ning (ML) demos that only seem to be rea­dy for pro­duc­tion but mis­cal­cu­la­te the requi­re­ments to have an ML sys­tem with a long and pros­pe­rous life. The aim of an ML pro­to­ty­pe is to pro­ve that an algo­rithm can do a bet­ter (che­a­per or fas­ter) job than alter­na­ti­ve solu­ti­ons. Howe­ver, moving ML pro­to­ty­pes into pro­duc­tion readi­ness needs dif­fe­rent skill sets, archi­tec­tures, and data inte­gra­ti­on stra­te­gies sin­ce, in real-world app­li­ca­ti­ons, sca­la­bi­li­ty, per­for­mance, and ope­ra­tio­nal cost-effi­ci­en­cy are key busi­ness-value dri­vers that will not come by pro­to­ty­pe design.

The goal of our work­shops is to show the dif­fe­ren­ces bet­ween ML pro­to­ty­pes and ML real-world app­li­ca­ti­ons on a case-by-case basis.

In order to have a bet­ter groun­ding, Mike and Jonas from the AI design firm 33A will start with their design can­vas to show how they sup­port com­pa­nies in their trans­for­ma­ti­on towards AI.

Hel­den­kom­bi­nat Tech­no­lo­gies’ AI experts will fol­low up in the second half of this work­shop and dive deeper into the ide­as gene­ra­ted by the par­ti­ci­pants by eva­lua­ting their tech­ni­cal fea­si­bi­li­ty and orga­ni­za­tio­nal requi­re­ments.

www.33a.ai

www.heldenkombinat.com

The work­shop is free of char­ge. We will announ­ce short­ly the new date for the work­shop.
The work­shop will be held in Eng­lish.

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