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Achieve multi-Region resiliency for your conversational AI chatbots with Amazon Lex

AWS Machine Learning

Enabling Global Resiliency for an Amazon Lex bot is straightforward using the AWS Management Console , AWS Command Line Interface (AWS CLI), or APIs. Global Resiliency APIs Global Resiliency provides API support to create and manage replicas. To better understand the solution, refer to the following architecture diagram.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

Using SageMaker with MLflow to track experiments The fully managed MLflow capability on SageMaker is built around three core components: MLflow tracking server This component can be quickly set up through the Amazon SageMaker Studio interface or using the API for more granular configurations.

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Japanese Telecommunications Giants KDDI Evolva & Terilogy Partner with TechSee to Launch “Video Support Service”

TechSee

TechSee, which enables remote support without dispatching technicians or other human resources, is already used by five of the top ten European and North American telecommunications carriers. WebRTC (Web Real-Time Communication) is a mechanism that enables real-time communication via API to web browsers and mobile applications.

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From innovation to impact: How AWS and NVIDIA enable real-world generative AI success

AWS Machine Learning

They use a highly optimized inference stack built with NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server to serve both their search application and pplx-api, their public API service that gives developers access to their proprietary models. The results speak for themselvestheir inference stack achieves up to 3.1

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Unlocking insights and enhancing customer service: Intact’s transformative AI journey with AWS

AWS Machine Learning

Frontend and API The CQ application offers a robust search interface specially crafted for call quality agents, equipping them with powerful auditing capabilities for call analysis. Additionally, the application offers backend dashboards tailored to MLOps functionalities, ensuring smooth monitoring and optimization of machine learning models.

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Accelerating ML experimentation with enhanced security: AWS PrivateLink support for Amazon SageMaker with MLflow

AWS Machine Learning

Note that MLflow tracking starts from the mlflow.start_run() API. The mlflow.autolog() API can automatically log information such as metrics, parameters, and artifacts. He holds a PhD in Telecommunications Engineering and has experience in software engineering. We specify the security group and subnets information in VpcConfig.

Metrics 87
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Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker

AWS Machine Learning

Programmatic setup Alternatively, you can create your labeling job programmatically using the CreateLabelingJob API. Whether you choose the SageMaker console or API approach, the result is the same: a fully configured labeling job ready for your annotation team. He has MBA from the Indian School of Business and B.

APIs 87