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Oct 11


Life beyond ChatGPT: from RAG to custom models with enterprise data LDN23

Large Language Models (LLMs)are transforming the way we interact with information, revolutionizing both internal knowledge management and customer-facing documentation and support.
Join Maria for a 2 part masterclass as she will discuss all about open and proprietary LLMs. The masterclass will deep dive into a RAG (Retrieval Augmented Generation) use case, a low hanging fruit when it comes to LLM use cases.
In the second part Maria will explain how to fine-tune foundational models on your company’s data and when to best to pursue this option. Finally she will discuss complexity, cost and data and will finish with a deep dive into an instruction fine tuning exercise.

13:00 - 15:00

Classroom B

Maria Zervou

Senior Specialist Solutions Architect

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Databricks has introduced new AI innovations within its Lakehouse platform, enabling customers to develop generative AI applications, including large language models (LLMs), with ease. These innovations include Vector Search, a curated collection of open-source models, LLM-optimized Model Serving, MLflow 2.5 with LLM capabilities, and Lakehouse Monitoring. By unifying the data and AI platform, Databricks simplifies the process of developing and deploying generative AI solutions, allowing organizations to accelerate their AI journey. This integrated approach offers benefits such as improved data quality, faster development, and effective governance. JetBlue, for example, has leveraged Databricks' platform to build their own LLM, enhancing decision-making using real-time data streams and improving user onboarding times.

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