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Building an Optimized ML Pipeline: The builders behind Superbet’s profanity detection use case
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Join us for a live webinar hosted by Qwak and featuring members of Happening's data science and ML engineering teams discussing how they built Superbet's profanity detection model.
During this session, we will discuss:
✔️ How the team ensured fallback variations for multi-language models without duplicating efforts
✔️ What best practices have been implemented for optimizing workflows and reducing operational efforts when re-deploying ML models.
✔️ How all this was implemented in Superbest's profanity model architecture, designed to identify profane messages in chat messages.
This will give you valuable insights into how Happening manages their ML pipeline and how you can optimize your own.
If you're a data scientist, ML engineer, or anyone interested in learning how to optimize ML pipelines for multiple audiences, this webinar is for you. Don't miss this opportunity to learn from the experts at Happening and improve your ML pipeline.
During this session, we will discuss:
✔️ How the team ensured fallback variations for multi-language models without duplicating efforts
✔️ What best practices have been implemented for optimizing workflows and reducing operational efforts when re-deploying ML models.
✔️ How all this was implemented in Superbest's profanity model architecture, designed to identify profane messages in chat messages.
This will give you valuable insights into how Happening manages their ML pipeline and how you can optimize your own.
If you're a data scientist, ML engineer, or anyone interested in learning how to optimize ML pipelines for multiple audiences, this webinar is for you. Don't miss this opportunity to learn from the experts at Happening and improve your ML pipeline.
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