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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. Customers are building innovative generative AI applications using Amazon Bedrock APIs using their own proprietary data.

APIs 128
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Meet Sophie AI: The Future of Service

TechSee

Generative AI will be transformative for every enterprise. This is the vision behind Sophie AI, the world’s first multisensory AI platform for the enterprise. As a Generative AI enterprise platform, Sophie AI is built to secularly observe, learn and interact at scale, helping your agents, engineers and end-customers.

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Amazon SageMaker simplifies setting up SageMaker domain for enterprises to onboard their users to SageMaker

AWS Machine Learning

For example, MLOps engineers typically perform model deployment activities, whereas data scientists perform ML training and validation activities. As a platform administrator, you can use the updated user interface (UI) and APIs to onboard users faster, with the right security settings and infrastructure.

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The Next Generation of Service Automation: The Power of Computer Vision

TechSee

VI Studio allows enterprises to train fully customized computer vision models with incredible accuracy and detail. For example, typical computer vision models will identify a generic device (e.g. Below are a few examples. . Introducing VI Studio. a television). the make and model of the TV), parts (e.g.

APIs 109
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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge Bases for Amazon Bedrock supports multiple vector databases, including Amazon OpenSearch Serverless , Amazon Aurora , Pinecone, and Redis Enterprise Cloud. For enterprise implementations, Knowledge Bases supports AWS Key Management Service (AWS KMS) encryption, AWS CloudTrail integration, and more.

APIs 117
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Building a seamless customer feedback process with Hello Customer

Hello Customer

So, why should your customer feedback process not be seamless and unified? You can integrate the Hello Customer platform with any of your existing systems, to combine customer feedback with other customer data, or trigger surveys automatically after an interaction. Why should you integrate your systems with Hello Customer?

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Building a seamless customer feedback process with Hello Customer

Hello Customer

So, why should your customer feedback process not be seamless and unified? You can integrate the Hello Customer platform with any of your existing systems, to combine customer feedback with other customer data, or trigger surveys automatically after an interaction. Why should you integrate your systems with Hello Customer?