Beyond Baseline Accuracy: Developing Industry-Ready LLMs
About the Event
The process of developing industry-ready models at scale requires an understanding of what training data to use, when to tune models, how to prompt them, and how to avoid errors and hallucinations. Join us for a compelling fireside chat with experts discussing how to get LLMs ready for industry in a way that works for your unique business problems. Get insights on the future of LLMs, the tooling landscape, and how to navigate the changes in this space.
The process of developing industry-ready models at scale requires an understanding of what training data to use, when to tune models, how to prompt them, and how to avoid errors and hallucinations. Join us for a compelling fireside chat with experts discussing how to get LLMs ready for industry in a way that works for your unique business problems. Get insights on the future of LLMs, the tooling landscape, and how to navigate the changes in this space.
Large Language Models are revolutionizing AI development. As companies strive to solve unique use cases, ML teams are facing the challenge of fine-tuning their models or using prompt engineering to elevate Foundation Models beyond their baseline accuracy on business-specific tasks.
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What to expect
An overview of the current state of LLMs and how they can be tuned to solve unique industry use cases that aren’t understood out of the box.
Live panel discussion covering the future of AI safety, prompt engineering, model tuning on unique internal business data, and emerging tools for enhancing Foundation Models accuracy.
A live Q&A session.
Hosted by
Previously CEO at Aipoly - First smartphone engine for convolutional neural networks. Management & Stats grad at Cass Business School and Singularity University. Never had a real job.
Raza Habib is on the front lines of LLM implementation as co-founder and CEO of Humanloop, a Y Combinator-backed startup that helps companies bridge the gap from API access to successful LLM deployment.
David co-founded Lakera after spending over a decade developing ML products. His team is focused on equipping development teams with the tools to put safety, security, and ethics at the core of AI models.
We’re leading the era of AI-first software
We help companies build software driven by deep learning, to make it accurate, reliable, and indistinguishable from magic.
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