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Deploy Gradio Apps on Hugging Face Spaces

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Jump Right To The Downloads Section Need Help Configuring Your Development Environment? Hugging Face Spaces is a platform for deploying and sharing machine learning (ML) applications with the community. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

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10 GitHub Awesome Lists for Data Science

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It is ideal for data science projects, machine learning experiments, and anyone who wants to work with real-world data. After Kaggle, this is one of the best sources for free datasets to download and enhance your data science portfolio. Perfect for hands-on learners who want to deepen their understanding through practical examples.

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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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Supercharge your auto scaling for generative AI inference – Introducing Container Caching in SageMaker Inference

AWS Machine Learning Blog

These improvements are available across a wide range of SageMaker’s Deep Learning Containers (DLCs), including Large Model Inference (LMI, powered by vLLM and multiple other frameworks), Hugging Face Text Generation Inference (TGI), PyTorch (Powered by TorchServe), and NVIDIA Triton.

AI 108
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Llama 3.3 70B now available in Amazon SageMaker JumpStart

AWS Machine Learning Blog

Getting started with SageMaker JumpStart SageMaker JumpStart is a machine learning (ML) hub that can help accelerate your ML journey. This feature eliminates one of the major bottlenecks in deployment scaling by pre-caching container images, removing the need for time-consuming downloads when adding new instances.

AWS 101
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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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I Won $10,000 in a Machine Learning Competition — Here’s My Complete Strategy

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The world’s leading publication for data science, AI, and ML professionals. Getting Started: You Don’t Need Expensive Hardware Let me get this clear, you don’t necessarily need an expensive cloud computing setup to win ML competitions (unless the dataset is too big to fit locally).