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How HSR.health is limiting risks of disease spillover from animals to humans using Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

This is a guest post co-authored by Ajay K Gupta, Jean Felipe Teotonio and Paul A Churchyard from HSR.health. The figure on the right highlights the zoonotic spillover risk severity levels within the regions covered, ranging from highest (red) to the lowest (dark green) risk.

ML 78
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Fine-tune Llama 2 for text generation on Amazon SageMaker JumpStart

AWS Machine Learning Blog

The authors would like to acknowledge the technical contributions of Christopher Whitten, Xin Huang, Kyle Ulrich, Sifei Li, Amy You, Adam Kozdrowicz, Evan Kravitz , Benjamin Crabtree, Haotian An, Manan Shah, Tony Cruz, Ernev Sharma, Jonathan Guinegagne and June Won. Person2#: This is green tea, you can drink it in summer.

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ML and NLP Research Highlights of 2021

Sebastian Ruder

Consequently, 2021 saw much discussion of best practices and ways in which we can reliably evaluate such models going forward, which I cover in this blog post. An art scene emerged around the most recent generation of generative models (see this blog post for an overview). ↩︎ Austin, J., Why is it important?  

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