Remove APIs Remove Benchmark Remove Calibration Remove Scripts
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Accelerate Amazon SageMaker inference with C6i Intel-based Amazon EC2 instances

AWS Machine Learning

Refer to the appendix for instance details and benchmark data. Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. Quantizing the model in PyTorch is possible with a few APIs from Intel PyTorch extensions.

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Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

AWS Machine Learning

Each trained model needs to be benchmarked against many tasks not only to assess its performances but also to compare it with other existing models, to identify areas that needs improvements and finally, to keep track of advancements in the field. Evaluating these models allows continuous model improvement, calibration and debugging.