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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

In the first part of the series, we showed how AI administrators can build a generative AI software as a service (SaaS) gateway to provide access to foundation models (FMs) on Amazon Bedrock to different lines of business (LOBs). It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

These insights are stored in a central repository, unlocking the ability for analytics teams to have a single view of interactions and use the data to formulate better sales and support strategies. You can invoke Lambda functions from over 200 AWS services and software-as-a-service (SaaS) applications.

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

AWS Machine Learning

Its software-as-a-service (SaaS) solution empowers leading banks and lenders with deep customer insights and AI-driven decision-making capabilities. They provide access to external data and APIs or enable specific actions and computation. At RDC, Hendra designs end-to-end analytics solutions within an Agile DevOps framework.

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Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications

AWS Machine Learning

A number of AWS independent software vendor (ISV) partners have already built integrations for users of their software as a service (SaaS) platforms to utilize SageMaker and its various features, including training, deployment, and the model registry.

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6 Ways to Vet Gen AI Vendors for the Contact Center

Interactions

Gen AI offers enormous potential for efficiency, knowledge sharing, and analytical insight in the contact center. There are several ways to work with Gen AI and LLMs, from SaaS applications with embedded Gen AI to custom-built LLMs to applications that bring in Gen AI and LLM capabilities via API. Is it an API model?

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3 steps to customer survey automation

delighted

Great examples of automated distribution include survey integrations and Application Programming Interface (API) connections. And, setting up APIs can link two applications to one another for data sharing/interacting purposes, making manual uploads a thing of the past. Create custom APIs for more complex use cases. Not to worry!

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Revolutionizing business processes with Amazon Bedrock and Appian’s generative AI skills

AWS Machine Learning

However, extracting meaningful insights from large datasets can be challenging without advanced analytical tools. This layer encapsulates the logic required to interact with the AWS AI services to manage API calls, data formatting, and error handling. Check out the Appian website to learn more about how to use the AI skills.