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Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

AWS Machine Learning Blog

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud.

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Why Microsoft is outspending big tech on Nvidia AI chips

Dataconomy

Amazon has announced plans to create a new data processing cluster featuring hundreds of thousands of its latest Trainium chips for Anthropic, showcasing a commitment to AI infrastructure. Additionally, Amazon is developing its Trainium and Inferentia chips. Featured image credit: Sam Torres/Unsplash

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Customize DeepSeek-R1 671b model using Amazon SageMaker HyperPod recipes – Part 2

AWS Machine Learning Blog

The following diagram illustrates the solution architecture for training using SageMaker HyperPod. With HyperPod, users can begin the process by connecting to the login/head node of the Slurm cluster. Alternatively, you can also use AWS Systems Manager and run a command such as the following to start the session.

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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

Orchestrate with Tecton-managed EMR clusters – After features are deployed, Tecton automatically creates the scheduling, provisioning, and orchestration needed for pipelines that can run on Amazon EMR compute engines. You can view and create EMR clusters directly through the SageMaker notebook.

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Who Said What? Recorder's On-device Solution for Labeling Speakers

Google Research AI blog

This feature is powered by Google's new speaker diarization system named Turn-to-Diarize , which was first presented at ICASSP 2022. Architecture of the Turn-to-Diarize system. It also reduces the total number of embeddings to be clustered, thus making the clustering step less expensive.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

Solution overview The following figure illustrates our system architecture for CreditAI on AWS, with two key paths: the document ingestion and content extraction workflow, and the Q&A workflow for live user query response. In the following sections, we dive into crucial details within key components in our solution.

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Transforming the future: A journey into model-based systems engineering at Singapore Institute of Technology

IBM Journey to AI blog

Students chose a system to model, defined modelling goals, and demonstrated their skills in various activities, including mock pitching MBSE to engineering organizations, defining system architecture and creating advanced model simulations.