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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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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

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Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

AWS Machine Learning Blog

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. PyTorch supports dynamic computational graphs, enabling network behavior to be changed at runtime. xlarge instance.

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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning Blog

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. With increased access to data, ML has the potential to provide unparalleled business insights and opportunities.

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Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio

AWS Machine Learning Blog

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. For more information, refer to Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements.

ML 130
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Researchers from Waabi and the University of Toronto Introduce LabelFormer: An Efficient Transformer-Based AI Model to Refine Object Trajectories for Auto-Labelling

Marktechpost

Auto-labeling methods that automatically produce sensor data labels have recently gained more attention. Auto-labeling may provide far bigger datasets at a fraction of the expense of human annotation if its computational cost is less than that of human annotation and the labels it produces are of comparable quality.

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Optimize hosting DeepSeek-R1 distilled models with Hugging Face TGI on Amazon SageMaker AI

AWS Machine Learning Blog

MAX_BATCH_PREFILL_TOKENS : This parameter caps the total number of tokens processed during the prefill stage across all batched requests, a phase that is both memory-intensive and compute-bound, thereby optimizing resource utilization and preventing out-of-memory errors. The best performance was observed on ml.p4dn.24xlarge 48xlarge , ml.g6e.12xlarge

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