article thumbnail

Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

As you build applications on AWS, aligning RAG applications with the AWS Well-Architected Framework provides a solid foundation for building enterprise-grade solutions that drive business value while adhering to industry standards. She speaks at internal and external conferences such as AWS re:Invent, AWS Summits, and webinars.

APIs 108
article thumbnail

Gemma is now available in Amazon SageMaker JumpStart 

AWS Machine Learning

. * The `if __name__ == "__main__"` block checks if the script is being run directly or imported. To run the script, you can use the following command: ``` python hello.py ``` * The output will be printed in the console: ``` Hello, world! ) # For the other hyperparameters, see the GitHub notebook attached in this blog.

Benchmark 111
article thumbnail

Best practices to build generative AI applications on AWS

AWS Machine Learning

Model customization has higher complexity than prompt engineering and RAG because the model’s weight and parameters are being changed via tuning scripts, which requires data science and ML expertise. This optimization of the model for its intended use allows you to deploy FMs successfully in real applications.