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That’s when Charles Burton, a data systems engineer for a company called CyberGRX, found out about it and refactored his workflow, reducing the processing time for his machine learning (ML) processing job from 8 days to 56 minutes… In this case, that means continuing to improve the model and the processes for one of the key offerings from CyberGRX, a cyber risk assessment of third parties usi…

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Recent large language models (LLMs) have enabled tremendous progress in natural language understanding… Instead of collecting the reasoning chains from scratch by asking humans, we instead learn from rich human feedback on model-generated reasoning chains using the prompting abilities of the LLMs…
Solution overview
With the onset of large language models, the field has seen tremendous progre…

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The torch…h header needs to be included when defining the kernel for you to have access to a NeuronCore-ported subset of the Pytorch C++ API:

#include <torch/torch…h header from the torchneuron library:

#include “torchneuron/register… If supplying the build_directory parameter, the library file will be stored in the indicated directory:

import torch_neuronx
from torch_neuron…

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This post describes how to implement your first ML use case using Amazon SageMaker in just 8–12 weeks by leveraging a methodology called Experience-based Acceleration (EBA)…

Solution overview: Machine Learning Experience-based Acceleration (ML EBA)
Machine learning EBA is a 3-day, sprint-based, interactive workshop (called a party) that uses SageMaker to accelerate business outcomes by …

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It helps customers review and improve their cloud-based architectures and better understand the business impact of their design decisions…
Take me to explore the Containers Build Lens!

Follow Containers Build Lens Best practices to architect your containers-based workloads…

Architecting for resiliency on AWS App Runner
Learn how to architect an highly available and resilient applicat…

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We recently introduced a new capability in the Amazon SageMaker Python SDK that lets data scientists run their machine learning (ML) code authored in their preferred integrated developer environment (IDE) and notebooks along with the associated runtime dependencies as Amazon SageMaker training jobs with minimal code changes to the experimentation done locally… Amazon SageMaker Model Training hel…