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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

The solution integrates large language models (LLMs) with your organization’s data and provides an intelligent chat assistant that understands conversation context and provides relevant, interactive responses directly within the Google Chat interface. The following figure illustrates the high-level design of the solution.

APIs 124
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Transforming credit decisions using generative AI with Rich Data Co and AWS

AWS Machine Learning

The mission of Rich Data Co (RDC) is to broaden access to sustainable credit globally. Making credit decisions using AI can be challenging, requiring data science and portfolio teams to synthesize complex subject matter information and collaborate productively. In this example, we start with the data science or portfolio agent.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. The model is then A/B tested along with the use case in pre-production with production-like data settings and approved for deployment to the next stage.

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How Formula 1® uses generative AI to accelerate race-day issue resolution

AWS Machine Learning

During these live events, F1 IT engineers must triage critical issues across its services, such as network degradation to one of its APIs. This impacts downstream services that consume data from the API, including products such as F1 TV, which offer live and on-demand coverage of every race as well as real-time telemetry.

APIs 73
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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

While these models are trained on vast amounts of generic data, they often lack the organization-specific context and up-to-date information needed for accurate responses in business settings. The function checks the semantic cache (Amazon Bedrock Knowledge Bases) using the Retrieve API. which is received by the Invoke Agent function.

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning

By using SnapLogic’s library of more than 800 pre-built connectors and data transformation capabilities, users can seamlessly integrate various data sources and AI models, dramatically accelerating the development process compared to traditional coding methods. Not anymore! The following demo shows Agent Creator in action.

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Generating value from enterprise data: Best practices for Text2SQL and generative AI

AWS Machine Learning

One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing with complex technical code, business users and data analysts can ask questions related to data and insights in plain language.