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Declarative MLOps - Streamlining Model Serving on Kubernetes
# Declarative MLOps
# Model Serving
# Kubernetes
Data Scientists prefer Jupyter Notebooks to experiment and train ML models. Serving these models in production can benefit from a more streamlined approach that can guarantee a repeatable, scalable, and high velocity. Kubernetes provides such an environment. And while third-party solutions for serving models make it easier, this talk demystifies how native K8s operators can be used to deploy models along with best practices for containerizing your own model, and CI/CD using GitOps.
Speakers
Rahul Parundekar
Founder @ A.I. Hero, Inc.
Agenda
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From4:15 PM, GMT
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Presentation
Declarative MLOps - Streamlining Model Serving on Kubernetes
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From4:55 PM, GMT
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1:1 networking
Networking
Event has finished
April 12, 4:00 PM, GMT
Online
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MLOps Community
Event has finished
April 12, 4:00 PM, GMT
Online
Organized by

MLOps Community