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10 Best AI Tools for Small Manufacturers (February 2025)

Unite.AI

This helps teams save time on training or looking up information, allowing them to focus on core operations. The system automatically tracks stock movements and allocates materials to orders (using a smart auto-booking engine) to maintain optimal inventory levels.

AI Tools 260
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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.

DevOps 103
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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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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.

ML 97
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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Complete the following steps: Choose Prepare and analyze data. Complete the following steps: Choose Run Data quality and insights report. Choose Create.

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How VMware built an MLOps pipeline from scratch using GitLab, Amazon MWAA, and Amazon SageMaker

Flipboard

We orchestrate our ML training and deployment pipelines using Amazon Managed Workflows for Apache Airflow (Amazon MWAA), which enables us to focus more on programmatically authoring workflows and pipelines without having to worry about auto scaling or infrastructure maintenance.

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Optimizing MLOps for Sustainability

AWS Machine Learning Blog

In addition to evaluating the accuracy of your models, processing jobs help you to make informed decisions about the tradeoffs between a model’s accuracy and its carbon footprint. Next, you can use governance to share information about the environmental impact of your model.