Artificial Intelligence
Author: Randy DeFauw
Randy DeFauw is a Sr. Principal Solutions Architect at AWS. He has over 20 years of experience in technology, starting with his university work on autonomous vehicles. He has worked with and for users ranging from startups to Fortune 50 companies, launching Big Data and Machine Learning applications. He holds an MSEE and an MBA, serves as a board advisor to K-12 STEM education initiatives, and has spoken at leading conferences including Strata and GlueCon. He is the co-author of the book SageMaker Best Practices.
Incorporating responsible AI into generative AI project prioritization
In this post, we explore how companies can systematically incorporate responsible AI practices into their generative AI project prioritization methodology to better evaluate business value against costs while addressing novel risks like hallucination and regulatory compliance. The post demonstrates through a practical example how conducting upfront responsible AI risk assessments can significantly change project rankings by revealing substantial mitigation work that affects overall project complexity and timeline.
Generate financial industry-specific insights using generative AI and in-context fine-tuning
In this blog post, we demonstrate prompt engineering techniques to generate accurate and relevant analysis of tabular data using industry-specific language. This is done by providing large language models (LLMs) in-context sample data with features and labels in the prompt. The results are similar to fine-tuning LLMs without the complexities of fine-tuning models.
Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker
This post is co-written with Chaoyang He, Al Nevarez and Salman Avestimehr from FedML. Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities. However, the sharing of […]
Use a generative AI foundation model for summarization and question answering using your own data
Large language models (LLMs) can be used to analyze complex documents and provide summaries and answers to questions. The post Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data describes how to fine-tune an LLM using your own dataset. Once you have a solid LLM, you’ll want to expose that LLM to […]
Use Serverless Inference to reduce testing costs in your MLOps pipelines
Amazon SageMaker Serverless Inference is an inference option that enables you to easily deploy machine learning (ML) models for inference without having to configure or manage the underlying infrastructure. SageMaker Serverless Inference is ideal for applications with intermittent or unpredictable traffic. In this post, you’ll see how to use SageMaker Serverless Inference to reduce cost when […]