Generative AI (GAI) using Elastic & Amazon SageMaker JumpStart
Blog post from Elastic
The blog post explores how to create a generative artificial intelligence (GAI) application using Amazon SageMaker JumpStart, Elasticsearch, and open-source large language models (LLMs) from Hugging Face. The combination of these technologies enables businesses to build AI solutions that generate original content across various domains while ensuring data security and cost-effectiveness. By leveraging Elasticsearch's capabilities in data retrieval and Amazon SageMaker's model hosting, the post outlines the implementation of a Retrieval Augmented Generation (RAG) system that efficiently handles domain-specific data, enhancing LLM responses with contextual relevance. The post also details the setup and deployment process, including the use of various tools and services such as Python, LangChain, Streamlit, and AWS Identity and Access Management, to create a seamless and effective AI-powered search experience.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 24 | 1,935 | 244 | 98 | -1% |
| RAG | 20 | 144 | 33 | 19 | -9% |
| Vector Search | 7 | 1,161 | 174 | 75 | -27% |
| AI Agents | 2 | 71 | 24 | 11 | -25% |
| Data Pipeline | 1 | 312 | 104 | 54 | -44% |
| Real-time | 1 | 2,035 | 534 | 182 | -15% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.