Video of the Week
Building an Optimized ML Pipeline: The builders behind Superbet’s profanity detection use case
Join us for a live webinar hosted by Qwak and featuring members of Happening’s data science and ML engineering teams as they discuss 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.
Previous Video of the Week
Thomas Scialom, PhD – Large Language Models: Past, Present and Future
In this keynote, Thomas Scialom will discuss the recent development for LLMs, from the transformers to GPT-4 and Llama 2, before presenting a deep dive into the future research trends that he anticipates in the field. Thomas Scialom, PhD is an A.I. Research Scientist at Meta. He is behind some of the world’s best-known Generative A.I. projects including Llama 2, BLOOM, Toolformer and Galactica. He is contributing to the development of Artificial General Intelligence (AGI). Thomas has lectured at many of the top A.I. labs (e.g., Google, Stanford, MILA). He holds a PhD from Sorbonne University, where he specialized in Natural-Language Generation with Reinforcement Learning.
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