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Reinventing a cloud-native federated learning architecture on AWS

AWS Machine Learning Blog

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. It is challenging to centralize such data for ML due to privacy requirements, high cost of data transfer, or operational complexity. The ML framework used at FL clients is TensorFlow.

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Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

AWS Machine Learning Blog

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

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Ready to pick up the chatbot’s call?

Dataconomy

The concept encapsulates a broad range of AI-enabled abilities, from Natural Language Processing (NLP) to machine learning (ML), aimed at empowering computers to engage in meaningful, human-like dialogue. But what exactly is conversational intelligence, and why is it so crucial in today’s tech-driven world?

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Amazon EC2 P5e instances are generally available

AWS Machine Learning Blog

Additionally, network latency can become an issue for ML workloads on distributed systems, because data needs to be transferred between multiple machines. DLAMI provides ML practitioners and researchers with the infrastructure and tools to quickly build scalable, secure, distributed ML applications in preconfigured environments.

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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

AWS Machine Learning Blog

In our pipeline, we used Amazon Bedrock to develop a sentence shortening algorithm for automatic time scaling. Here’s the shortened sentence using the sentence shortening algorithm. She specializes in leveraging AI and ML to drive innovation and develop solutions on AWS. Partner Solutions Architect at AWS, specializing in AI/ML.

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Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

AWS Machine Learning Blog

JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19).

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Predicting new and existing product sales in semiconductors using Amazon Forecast

AWS Machine Learning Blog

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We also demonstrate the performance of our state-of-the-art point cloud-based product lifecycle prediction algorithm.