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Accelerate NLP inference with ONNX Runtime on AWS Graviton processors

AWS Machine Learning

We also demonstrate the resulting speedup through benchmarking. Benchmark setup We used an AWS Graviton3-based c7g.4xl 1014-aws kernel) The ONNX Runtime repo provides inference benchmarking scripts for transformers-based language models. The scripts support a wide range of models, frameworks, and formats.

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Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together

AWS Machine Learning

It can navigate open-ended prompts, and novel scenarios with remarkable fluency, including task automation, hypothesis generation, and analysis of charts, graphs, and forecasts. Media organizations can generate image captions or video scripts automatically. And Sonnet is first available on Amazon Bedrock today.

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Optimized PyTorch 2.0 inference with AWS Graviton processors

AWS Machine Learning

wheels and set the previously mentioned environment variables # Clone PyTorch benchmark repo git clone [link] # Setup Resnet50 benchmark cd benchmark python3 install.py On successful completion of the inference runs, # the script prints the inference latency and accuracy results python3 run.py

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Reduce Amazon SageMaker inference cost with AWS Graviton

AWS Machine Learning

We cover computer vision (CV), natural language processing (NLP), classification, and ranking scenarios for models and ml.c6g, ml.c7g, ml.c5, and ml.c6i SageMaker instances for benchmarking. You can use the sample notebook to run the benchmarks and reproduce the results. Create an endpoint configuration.

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5 Tips To Reduce Your Call Center’s Average Handle Time (AHT)

Global Response

While this varies some by industry, 6 minutes is a standard benchmark to aim for in the beginning. Understanding why scripts and knowledge bases should be improved is just the first step, though—how should companies actually begin to do so? What scripts or key language or techniques were used in these calls? Check QA logs.

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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. Benchmark data The following table compares the cost and relative performance between c5 and c6 instances.

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Mitigate Agent, Customer, Operational and Regulatory Risk

Enghouse Interactive

of business executives (2020 NTT/DiData Customer Experience Benchmarking Study), see the customer experience that their organizations provide as a key differentiator and the number one indicator of their overall strategic leadership. And it may be no more than an innocent, off-script comment that causes the issue.

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