blog_posts: 20d259ec45
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id | createdDate | title | link | postExcerpt | featuredImageUrl | hash | contributors | modifiedDate | displayDate |
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blog-posts#33-58580 | 2024-02-01 17:44:16 | Preprocess and fine-tune LLMs quickly and cost-effectively using Amazon EMR Serverless and Amazon SageMaker | https://aws.amazon.com/blogs/big-data/preprocess-and-fine-tune-llms-quickly-and-cost-effectively-using-amazon-emr-serverless-and-amazon-sagemaker/ | Large language models (LLMs) are becoming increasing popular, with new use cases constantly being explored. In general, you can build applications powered by LLMs by incorporating prompt engineering into your code. However, there are cases where prompting an existing LLM falls short. This is where model fine-tuning can help. Prompt engineering is about guiding the [...] | https://d2908q01vomqb2.cloudfront.net/b6692ea5df920cad691c20319a6fffd7a4a766b8/2024/01/14/BDB-3705-solution-archi-300x83.png | 20d259ec45 | Shijian Tang, Dalei Xu, Matthew Liem, Yuanjun Xiao | 2024-02-01 17:44:16 | 01 Feb 2024 |
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