AWS Database Demo 
APRIL 24, 2025
These tables house complex domain-specific schemas, with instances of nested tables and multi-dimensional data that require complex database queries and domain-specific knowledge for data retrieval. As a result, NL2SQL solutions for enterprise data are often incomplete or inaccurate.
AWS Machine Learning Blog
APRIL 7, 2025
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies and AWS. Solution overview The following diagram provides a high-level overview of AWS services and features through a sample use case.
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AWS Machine Learning Blog
OCTOBER 31, 2024
AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.
AWS Machine Learning Blog
NOVEMBER 7, 2024
The Lambda function runs the database query against the appropriate OpenSearch Service indexes, searching for exact matches or using fuzzy matching for partial information. For specific part inquiries, the agent consults the action groups available to the agent and invokes the correct action (API) to retrieve relevant information.
AWS Machine Learning Blog
NOVEMBER 14, 2024
In this post, we save the data in JSON format, but you can also choose to store it in your preferred SQL or NoSQL database. Prerequisites To perform this solution, complete the following: Create and activate an AWS account. Make sure your AWS credentials are configured correctly. Install Python 3.7
APRIL 23, 2025
To assist in this effort, AWS provides a range of generative AI security strategies that you can use to create appropriate threat models. For all data stored in Amazon Bedrock, the AWS shared responsibility model applies. The high-level steps are as follows: For our demo , we use a web application UI built using Streamlit.
IBM Journey to AI blog
MARCH 14, 2024
Also, traditional database management tasks, including backups, upgrades and routine maintenance drain valuable time and resources, hindering innovation. By using fit-for-purpose databases, customers can efficiently run workloads, using the appropriate engine at the optimal cost to optimize analytics for the best price-performance.
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