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Integrate HyperPod clusters with Active Directory for seamless multi-user login

AWS Machine Learning Blog

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. In this solution, HyperPod cluster instances use the LDAPS protocol to connect to the AWS Managed Microsoft AD via an NLB.

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KDnuggets News, April 6: 8 Free MIT Courses to Learn Data Science Online; The Complete Collection Of Data Repositories – Part 1

KDnuggets

8 Free MIT Courses to Learn Data Science Online; The Complete Collection Of Data Repositories - Part 1; DBSCAN Clustering Algorithm in Machine Learning; Introductory Pandas Tutorial; People Management for AI: Building High-Velocity AI Teams.

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Google Research, 2022 & beyond: Algorithmic advances

Google Research AI blog

In 2022, we continued this journey, and advanced the state-of-the-art in several related areas. We continued our efforts in developing new algorithms for handling large datasets in various areas, including unsupervised and semi-supervised learning , graph-based learning , clustering , and large-scale optimization.

Algorithm 110
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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

For this post we’ll use a provisioned Amazon Redshift cluster. Set up the Amazon Redshift cluster We’ve created a CloudFormation template to set up the Amazon Redshift cluster. Implementation steps Load data to the Amazon Redshift cluster Connect to your Amazon Redshift cluster using Query Editor v2.

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DeepSeek R2 is coming fast: Can the West keep up?

Dataconomy

The firm allocated 70% of its revenue towards AI research, building two supercomputing AI clusters, including one consisting of 10,000 Nvidia A100 chips during 2020 and 2021. banned A100 chip exports to China in 2022. With limited competition for such resources, DeepSeek has attracted leading researchers.

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Differentially private clustering for large-scale datasets

Google Research AI blog

Posted by Vincent Cohen-Addad and Alessandro Epasto, Research Scientists, Google Research, Graph Mining team Clustering is a central problem in unsupervised machine learning (ML) with many applications across domains in both industry and academic research more broadly. When clustering is applied to personal data (e.g.,

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Building Meta’s GenAI Infrastructure

Hacker News

Marking a major investment in Meta’s AI future, we are announcing two 24k GPU clusters. We use this cluster design for Llama 3 training. We built these clusters on top of Grand Teton , OpenRack , and PyTorch and continue to push open innovation across the industry. The other cluster features an NVIDIA Quantum2 InfiniBand fabric.