Source: thenewstack.io
For AI to Succeed, MLOps Needs a Bridge to DevOps
https://www.linkedin.com/in/luis-ceze-50b2314 AI has been heralded as the new “brains” for software applications, a role long held by databases. Actually, incorporating https://thenewstack.io/category/machine-learning/ models (which power AI) in productivity-focused applications — to make them smarter — is overly difficult and complex.
If AI is to be the “brains” of applications, a world where ML models are heavily specialized, requiring unique and customized workflows and tools is problematic.
Deployment procedures need to be made accessible and workable for application developers, DevOps engineers and IT operations teams.
Application developers and DevOps teams should be an integral part of ML deployment, without becoming experts in machine learning.
If AI is to be the “brains” of applications, a world where ML models are heavily specialized, requiring unique and customized workflows and tools is problematic.
Deployment procedures need to be made accessible and workable for application developers, DevOps engineers and IT operations teams.
Application developers and DevOps teams should be an integral part of ML deployment, without becoming experts in machine learning.
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