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Streamlining NLP Model Creation and Inference

Posted Apr 12
# Model serving
# Model training
# Production Use Case
# Systems and Architecture
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SPEAKERS
Phillip North
Phillip North
Phillip North
Machine Learning Engineer @ Primer.ai

Phillip is an engineer at Primer working on the ML Platform team. Prior to Primer he has worked both as an engineer and data scientist at various small start-ups.

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Phillip is an engineer at Primer working on the ML Platform team. Prior to Primer he has worked both as an engineer and data scientist at various small start-ups.

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Cary Goltermann
Cary Goltermann
Cary Goltermann
Machine Learning Engineer @ Primer.ai

Cary is a software engineer at Primer where he works on the ML Platform team. Prior to joining Primer he worked for KPMG as a data scientist creating machine learning models and applications for tax professionals.

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Cary is a software engineer at Primer where he works on the ML Platform team. Prior to joining Primer he worked for KPMG as a data scientist creating machine learning models and applications for tax professionals.

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SUMMARY

At Primer we deliver applications with cutting-edge NLP models to surface actionable information from vast stores of unstructured text. The size of these models and our applicationsÕ latency requirements create an operational challenge of deploying a model as a service. Furthermore, creation/customization of these models for our customers is difficult as model training requires the procurement, setup, and use of specialized hardware and software. PrimerÕs ML Platform team solved both of these problems, model training and serving, by creating Kubernetes operators. In this talk we will discuss why we chose the Kubernetes operator pattern to solve these problems and how the operators are designed.

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