Title

AWS re:Invent 2023 - Build & deploy generative AI apps in days with Databricks Lakehouse AI (AIM236)

Summary

• Inna Kolova from Databricks and Bradley Axon from Block (formerly Square) discuss building generative AI applications.
• They cover data-centric generative AI, including techniques like augmentation, fine-tuning, and pre-training models.
• Databricks has seen immense growth in the use of transformers and large language models (LLMs), with applications across industries.
• The commoditization of model techniques and the rapid release of LLMs have led to intense competition and price drops.
• Open source community contributions are matching or exceeding proprietary model quality.
• Enterprises struggle with siloed data systems, leading to challenges in large-scale generative AI implementation.
• Databricks advocates for integrating generative AI and classical ML within a unified data platform.
• They introduce vector search and feature serving functionalities for data preparation.
• The importance of fine-tuning models for domain-specific tasks and reducing latency and costs is emphasized.
• Pre-training allows full control over data sources and model intellectual property.
• MLflow AI Gateway is introduced as a solution for managing multiple LLMs and vendor APIs.
• Databricks model serving unifies different model flavors and provides a single monitoring solution for data and ML.
• Bradley Axon shares Block's experience with Databricks tools, emphasizing the need for a flexible and quick integration platform.
• Block's use cases include action-based conversations, customer-facing chat experiences, and the importance of fine-tuning for performance and cost optimization.
• The session concludes with recommendations to start with prompt engineering and progress towards more advanced techniques iteratively.

Insights

• The rapid commoditization of AI model techniques and the proliferation of LLMs have democratized access to generative AI, but have also created challenges in differentiating services and managing costs.
• The open source community's contributions to AI models are significant, potentially reducing reliance on proprietary models and fostering innovation.
• Data silos within large enterprises are a significant barrier to effective generative AI deployment, highlighting the need for unified data platforms.
• Databricks' vector search and feature serving are critical for efficiently handling both structured and unstructured data in generative AI applications.
• Fine-tuning and pre-training models are not mutually exclusive; they can be combined for optimal performance in generative AI applications.
• MLflow AI Gateway serves as a central management point for multiple LLMs, addressing operational challenges such as API key management and cost monitoring.
• Block's experience underscores the importance of a flexible and scalable AI platform that can adapt to the rapidly evolving landscape and diverse business unit needs.
• The iterative approach to AI model development, starting with simple prompt engineering and advancing to more complex techniques, is a practical strategy for businesses to adopt AI incrementally.
• The session's content suggests a growing trend towards in-house AI capabilities, with a focus on optimizing performance, managing costs, and maintaining control over data and intellectual property.