Title

AWS re:Invent 2023 - Customize FMs for generative AI applications with Amazon Bedrock (AIM247)

Summary

Kishore Ahear, Principal Product Manager for Amazon Bedrock, introduced the concept of foundational model customization for generative AI applications.
Customization is necessary to adapt foundational models to specific business cases, domain-specific language, and improve context awareness.
Common approaches for customization include prompt engineering, retrieval augmented generation (RAG), and training models from scratch.
Amazon Bedrock supports customization through fine-tuning and continued pre-training, allowing models to learn task-specific performance or domain-specific data.
Dataset requirements for fine-tuning include prompts and completions, while continued pre-training only requires raw data.
Amazon Bedrock Custom Models can be deployed using provision throughput and support both first-party and third-party models for fine-tuning and first-party models for continued pre-training.
Security and privacy are ensured as data used to improve models is not shared with any model providers, and customer data remains within the Amazon region.
Anand Pradhan, Senior Director at NYSE, shared insights on generative AI implementation at NYSE, including the creation of an intelligent trading rule chatbot using Bedrock.
Chris, a Principal Solution Architect, demonstrated fine-tuning and continued pre-training with Amazon Bedrock, including the use of a new book as data for continued pre-training.

Customizing foundational models is crucial for businesses to ensure that AI applications produce results that are specific and relevant to their domain and use cases.
Amazon Bedrock provides a streamlined process for customizing models, which can be a game-changer for organizations looking to leverage generative AI without extensive infrastructure or data science expertise.
The ability to fine-tune and continually pre-train models with both labeled and unlabeled data offers flexibility in how organizations can approach customization.
The security and privacy features of Amazon Bedrock, such as data encryption and integration with IAM roles, are important for organizations concerned with data governance and compliance.
Real-world applications, such as NYSE's intelligent trading rule chatbot, demonstrate the practical benefits of using Amazon Bedrock for generative AI tasks.
The demonstrations by Chris highlight the practical steps involved in customizing models using Amazon Bedrock and the tangible improvements that can be achieved through fine-tuning and continued pre-training.