[Webinar] Lots of Little Mistakes: LLMs in Production

Are large language models technically just language models, but larger? The short answer is yes. The tasks they enable aren't necessarily new, but their ability to perform these tasks and the ease of deploying these models have both improved greatly in recent years. However, despite these architectural advances, there are still a number of challenges when it comes to actually deploying and productionalizing large language models. They still make "Lots of Little Mistakes" (and some big ones, too).

In this webinar, Arthur's Chief Scientist John Dickerson covered:
- Applications of LLMs in critical business areas like writing code, summarizing/generating text, customer servicing, and more
- The challenges of deploying LLMs safely, effectively, and ethically
- How to mitigate those challenges before, during, and after production
- Arthur's recent work in this space and how our newest product, Arthur Shield, can help companies quickly and safely deploy LLMs like ChatGPT

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About Arthur:
Arthur is the AI performance company. Our platform monitors, measures, and improves machine learning models to deliver better results. We help data scientists, product owners, and business leaders accelerate model operations and optimize for accuracy, explainability, and fairness.

Arthur’s research-led approach to product development drives exclusive capabilities in computer vision, NLP, bias mitigation, and other critical areas. We’re on a mission to make AI work for everyone, and we are deeply passionate about building ML technology to drive responsible business results.