To accelerate data discovery for DeepSeek-R1 objects using Amazon S3 Metadata, follow these steps:
1. Enable S3 Metadata: Ensure that S3 Metadata is enabled for your general-purpose buckets. This feature automatically captures metadata for objects in these buckets, including system-defined metadata like creation time and storage class, custom metadata such as tags, and event metadata like update or deletion events[1][3].
2. Store DeepSeek-R1 Objects in S3: Store your DeepSeek-R1 model files and related data in an S3 bucket. This can include model weights, training data, or any other relevant files. Use commands like `aws s3 sync` to upload your files to S3, excluding unnecessary directories like `.git`[5].
3. Apply Custom Metadata: Use object tags to apply custom metadata to your DeepSeek-R1 objects. This could include information like model versions, training datasets, or specific use cases. Custom metadata allows you to annotate objects with business-specific details, making them easier to query and manage[1][10].
4. Query Metadata with S3 Tables: S3 Metadata stores captured metadata in read-only Apache Iceberg tables, known as metadata tables. These tables are optimized for querying and can be integrated with AWS Glue Data Catalog. This integration allows you to query your metadata using services like Amazon Athena, Amazon EMR, or Amazon QuickSight[1][7].
5. Integrate with AWS Analytics Services: Use AWS analytics services to query and analyze your metadata. For example, you can use Amazon Athena to run SQL queries on your metadata tables, helping you quickly locate specific DeepSeek-R1 model versions or training datasets[1][10].
6. Monitor and Update Metadata: As you update or add new DeepSeek-R1 objects to your S3 bucket, S3 Metadata automatically refreshes the metadata tables to reflect these changes. This ensures that your metadata remains up-to-date and accurate, facilitating efficient data discovery and management[1][9].
By leveraging S3 Metadata in this way, you can streamline the process of managing and discovering DeepSeek-R1 objects in your S3 buckets, enhancing your ability to analyze and utilize these assets effectively.
Citations:
[1] https://docs.aws.amazon.com/AmazonS3/latest/userguide/metadata-tables-overview.html
[2] https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-models-on-amazon-sagemaker-using-a-large-model-inference-container/
[3] https://repost.aws/questions/QUg7ThXS_LTqK-N4DfASm20w/few-records-in-s3-metadata
[4] https://www.youtube.com/watch?v=pWekT7Ic6VE
[5] https://community.aws/content/2sIJqPaPMtmNxlRIQT5CzpTtziA/deploy-deepseek-r1-on-aws-bedrock?lang=en
[6] https://www.bigdatawire.com/2024/12/03/aws-unveils-hosted-apache-iceberg-service-on-s3-new-metadata-management-layer/
[7] https://finance.yahoo.com/news/amazon-s3-expands-capabilities-managed-165200173.html
[8] https://www.tigrisdata.com/blog/thoughts-deepseek-r1/
[9] https://press.aboutamazon.com/2024/12/amazon-s3-expands-capabilities-with-managed-apache-iceberg-tables-for-faster-data-lake-analytics-and-automatic-metadata-generation-to-simplify-data-discovery-and-understanding
[10] https://blocksandfiles.com/2025/01/31/amazon-web-services-makes-it-easier-and-faster-to-understand-s3-data-with-metadata-offering/
[11] https://aws.amazon.com/s3/features/metadata/