LinkedIns AI-skalering: Vil fordoble ML-udvikleres produktivitet

Foto : Natee Meepian / Bigstock

LinkedIns AI-skalering: Vil fordoble ML-udvikleres produktivitet

Hvis flere ML-modeller skal gøre gavn i produktionen hos LinkedIn, skal udviklerne være i stand til at afprøve flere ideer og udføre flere eksperimenter på kortere tid.  Derfor har det sociale medie iværksat et program, selskabet kalder Productive Machine Learning – eller blot ProML – der både er en strategi og en platform for at udvikle ML mere effektivt. 
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Nøgleideerne i LinkedIns ProML
  • We will leverage and improve best-of-breed components from our existing code base to the maximum extent feasible. We are unlikely to rewrite our entire tech stack, but any particular component is fair game.

  • The state of the art is constantly evolving with new algorithms and open source frameworks—we need to be flexible to support our existing major ML algorithms as well as new ones that will emerge.

  • We will use an agile-inspired strategy so that each step we take is delivering value by making at least one product line better or providing generally useable improvements to existing components.

  • The ability to run the models in real-time is as important as the ability to author or train them. The services hosting the models must be able to be independently upgraded without breaking their downstream or upstream services.

  • New models, retrained models, and models using new technologies must be A/B testable in production.

  • We must build GDPR privacy requirements into every stage of the solution.

Kilde: Linkedin.

 

LinkedIn i tal

LinkedIn har:

  • 650 millioner medlemmer
  • 30 millioner selskaber
  • 20 millioner job
  • 35 tusind skills

Selskabets knowledge graph rummer 53 milliarder graph edges, 1 milliard noder og over 100 millioner jobansøgninger om ugen.