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Future AGI Secures $1.6M to Launch the World’s Most Accurate AI Evaluation Platform

Unite.AI

Future AGIs proprietary technology includes advanced evaluation systems for text and images, agent optimizers, and auto-annotation tools that cut AI development time by up to 95%. Enterprises can complete evaluations in minutes, enabling AI systems to be optimized for production with minimal manual effort.

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10 Best AI Tools for Small Manufacturers (February 2025)

Unite.AI

The system automatically tracks stock movements and allocates materials to orders (using a smart auto-booking engine) to maintain optimal inventory levels. Key features of Katana: Live Inventory Control: Real-time tracking of raw materials and products with auto-booking to allocate stock to orders efficiently.

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From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams

Towards AI

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams Photo by Parabol | The Agile Meeting Toolbox on Unsplash In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers.

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Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

AWS Machine Learning Blog

Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring. They often work with DevOps engineers to operate those pipelines.

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

AWS Machine Learning Blog

With the SageMaker HyperPod auto-resume functionality, the service can dynamically swap out unhealthy nodes for spare ones to ensure the seamless continuation of the workload. Also included are SageMaker HyperPod cluster software packages, which support features such as cluster health check and auto-resume.

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Node problem detection and recovery for AWS Neuron nodes within Amazon EKS clusters

AWS Machine Learning Blog

By accelerating the speed of issue detection and remediation, it increases the reliability of your ML training and reduces the wasted time and cost due to hardware failure. This solution is applicable if you’re using managed nodes or self-managed node groups (which use Amazon EC2 Auto Scaling groups ) on Amazon EKS. and public.ecr.aws.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Optionally, if Account A and Account B are part of the same AWS Organizations, and the resource sharing is enabled within AWS Organizations, then the resource sharing invitation are auto accepted without any manual intervention. Following are the steps completed by using APIs to create and share a model package group across accounts.

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