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Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines

AWS Machine Learning

It is a sampled version of the “ Diabetes 130-US hospitals for years 1999-2008 Data Set”. Autopilot training jobs start their own dedicated SageMaker backend processes, and dedicated SageMaker API calls are required to start new training jobs, monitor training job statuses, and invoke trained Autopilot models. SageMaker pipeline steps.

Scripts 77
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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning

We make this possible in a few API calls in the JumpStart Industry SDK. Using the SageMaker API, we downloaded annual reports ( 10-K filings ; see How to Read a 10-K for more information) for a large number of companies. We select Amazon’s SEC filing reports for years 2021–2022 as the training data to fine-tune the GPT-J 6B model.

Finance 66
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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning

We make this possible in a few API calls in the JumpStart Industry SDK. Using the SageMaker API, we downloaded annual reports ( 10-K filings ; see How to Read a 10-K for more information) for a large number of companies. We select Amazon’s SEC filing reports for years 2021–2022 as the training data to fine-tune the GPT-J 6B model.

Finance 52