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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

AWS Machine Learning

However, traditional dubbing methods are costly ( about $20 per minute with human review effort ) and time consuming, making them a common challenge for companies in the Media & Entertainment (M&E) industry. Welocalize benchmarks the performance of using LLMs and machine translations and recommends using LLMs as a post-editing tool.

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How Games24x7 transformed their retraining MLOps pipelines with Amazon SageMaker

AWS Machine Learning

Games24x7 is one of India’s most valuable multi-game platforms and entertains over 100 million gamers across various skill games. The code to invoke the pipeline script is available in the Studio notebooks, and we can change the hyperparameters and input/output when invoking the pipeline.

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning

For benchmark analysis, we considered the task of predicting the in-hospital mortality of patients [2]. You can place the data in any folder of your choice, as long as the path is consistently referenced in the training script and has access enabled. Import the data loader into the training script. and data_loader.py

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Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

Laying the groundwork: Collecting ground truth data The foundation of any successful agent is high-quality ground truth data—the accurate, real-world observations used as reference for benchmarks and evaluating the performance of a model, algorithm, or system.

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Eight Great Call Center Practices Ideas That Are Worthy to Share In 2018

Dialer 360

Give Best Sales Script. Do you know that starts with the contents they are prepared to work from or in other words the sales script? Noteworthy time and cash ought to be spent on creating scripts and in addition tweaking them as calls are observed and issues end up clear. Introduce New Starters with Your Best Entertainers.

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

AWS Machine Learning

nn For performance benchmarking of different models on the Dolly and Dialogsum dataset, refer to the Performance benchmarking section in the appendix at the end of this post. Appendix This appendix provides additional information about performance benchmarking and dataset formatting.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

Some models may be trained on diverse text datasets like internet data, coding scripts, instructions, or human feedback. The final outcome will be aggregated results that combine the scores of all the outputs (calculate the average precision or human rating) and allow the users to benchmark the quality of the models.