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Agents as escalators: Real-time AI video monitoring with Amazon Bedrock Agents and video streams

Flipboard

You can add new actions by defining additional endpoint handlers following the same pattern and generating a new OpenAPI schema: ``` if __name__ == "__main__": print(app.get_openapi_json_schema()) ``` Text-to-SQL integration Through its action group, the agent is able to translate natural language queries into SQL for structured data analysis.

AWS 142
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Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas

AWS Machine Learning Blog

The workflow includes the following steps: Within the SageMaker Canvas interface, the user composes a SQL query to run against the GCP BigQuery data warehouse. To query from Athena, launch the Athena SQL editor and choose the data source you created. You can now use the connector in your Athena queries.

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Building the Benchmark: Inside Our Agentic Insurance Underwriting Dataset

Snorkel AI

So, they had to enter a chain of SQL queries to determine the correct criteria and thresholds, interacting with underwriters to obtain the information they needed in the process. If they can answer questions about quantum field theory, why can’t they figure out what columns are in a table before writing a sql query?

SQL 52
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

We had to migrate our AuthZ backend from Airbyte to native SQL replication so that it can support access management in near real time at scale. The integration of text-to-SQL will enable users to query structured databases using natural language, simplifying data access.

AWS 112
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Connect, share, and query where your data sits using Amazon SageMaker Unified Studio

Flipboard

The retail team has created a project retailsales-sql-project and the data analysts team has created a project dataanalyst-sql-project within SageMaker Unified Studio. Create a SageMaker Unified Studio domain and three projects using the SQL analytics project profile. See Create a new project to create a project.

SQL 140
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Evaluating AI Agents for Insurance Underwriting

Snorkel AI

So, they had to enter a chain of SQL queries to determine the correct criteria and thresholds, interacting with underwriters to obtain the information they needed in the process. For example, were SQL queries properly written and executed? However, the correct sequence of tool use behavior was sometimes very complex.

AI 59
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Evaluating AI Agents for Insurance Underwriting

Snorkel AI

So, they had to enter a chain of SQL queries to determine the correct criteria and thresholds, interacting with underwriters to obtain the information they needed in the process. For example, were SQL queries properly written and executed? However, the correct sequence of tool use behavior was sometimes very complex.

AI 59