# LSOPS13-BP01 Store data in an AI/ML-ready formats
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 Expectations of what can be done with data are rapidly expanding. Life sciences projects produce rich data. Work to maximize data storage to allow for new technologies such as AI/ML. 

 **Desired outcome:** Be able to pivot and adapt as there are new technological demands on existing systems and data. 

 **Level of risk exposed if this best practice is not established:** Low 

## Implementation guidance
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 Use data processing that stores data in adaptable, open standard formats. This will allow for the greatest flexibility as technologies progress. 

### Implementation steps
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1.  Store data in document formats ingestible by Amazon Sagemaker and Amazon Bedrock. 

1.  Use AWS Glue to transform data into Iceberg and place in an Amazon S3 data lake. 

## Resources
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 **Related documents:** 
+  [Accelerating Life Sciences Innovation with Agentic AI on AWS](https://aws.amazon.com/blogs/industries/accelerating-life-sciences-innovation-with-agentic-ai-on-aws/) 
+  [What is Apache Iceberg?](https://aws.amazon.com/what-is/apache-iceberg/) 
+  [Building an End-to-End Data Strategy for Analytics and Generative AI: Insights from AWS and Workhuman](https://aws.amazon.com/awstv/watch/f4743296958/) 

 **Related examples:** 
+  [Use generative AI on AWS for efficient clinical document analysis](https://aws.amazon.com/blogs/architecture/use-generative-ai-on-aws-for-efficient-clinical-document-analysis/) 
+  [Revolutionizing clinical trials with the power of voice and AI](https://aws.amazon.com/blogs/machine-learning/revolutionizing-clinical-trials-with-the-power-of-voice-and-ai/) 

 **Related tools:** 
+  [Amazon Bedrock](https://aws.amazon.com/bedrock/) 
+  [Amazon SageMaker AI](https://aws.amazon.com/sagemaker/)