In our previous blog posts, we explored various techniques such as fine-tuning large language models (LLMs), prompt engineering, and Retrieval Augmented Generation (RAG) using Amazon Bedrock to generate impressions from the findings section in radiology reports using generative AI. Part 1 focused on model...
This is a guest post authored by the team at ByteDance.
ByteDance is a technology company that operates a range of content platforms to inform, educate, entertain, and inspire people across languages, cultures, and geographies. Users trust and enjoy our content platforms because of the...
In enterprise environments, organizations often divide their AI operations into two specialized teams: an AI research team and a model hosting team. The research team is dedicated to developing and enhancing AI models using model training and fine-tuning techniques. Meanwhile, a separate hosting team...
Brands today are juggling a million things, and keeping product content up-to-date is at the top of the list. Between decoding the endless requirements of different marketplaces, wrangling inventory across channels, adjusting product listings to catch a customer’s eye, and trying to outpace shifting...
This blog post is co-written with Gene Arnold from Alation.
To build a generative AI-based conversational application integrated with relevant data sources, an enterprise needs to invest time, money, and people. First, you would need build connectors to the data sources. Next you need to...
Troubleshooting infrastructure as code (IaC) errors often consumes valuable time and resources. Developers can spend multiple cycles searching for solutions across forums, troubleshooting repetitive issues, or trying to identify the root cause. These delays can lead to missed security errors or compliance violations, especially...
