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Map Earth’s vegetation in under 20 minutes with Amazon SageMaker

AWS Machine Learning Blog

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning Blog

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. Create a custom container image for ML model training and push it to Amazon ECR.

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Train and deploy ML models in a multicloud environment using Amazon SageMaker

AWS Machine Learning Blog

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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Computer Vision and Deep Learning for Education

PyImageSearch

This last blog of the series will cover the benefits, applications, challenges, and tradeoffs of using deep learning in the education sector. To learn about Computer Vision and Deep Learning for Education, just keep reading. As soon as the system adapts to human wants, it automates the learning process accordingly.

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PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

AWS Machine Learning Blog

Trainium chips are purpose-built for deep learning training of 100 billion and larger parameter models. Model training on Trainium is supported by the AWS Neuron SDK, which provides compiler, runtime, and profiling tools that unlock high-performance and cost-effective deep learning acceleration. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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Getting Started with YOLO11

PyImageSearch

To learn how to master YOLO11 and harness its capabilities for various computer vision tasks , just keep reading. Jump Right To The Downloads Section What Is YOLO11? VideoCapture(input_video_path) Next, we download the input video from the pyimagesearch/images-and-videos repository using the hf_hub_download() function.

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

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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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