| Name | Version | Summary | date |
| sagemaker-studio-dataengineering-extensions |
1.2.1 |
A python Package to enhance experience of SageMaker Unified Studio |
2025-07-26 21:16:32 |
| sagemaker-studio-dataengineering-sessions |
1.2.1 |
A python Package to run Spark code in different AWS Compute |
2025-07-26 21:16:27 |
| sagemaker-studio |
1.0.20 |
Python library to interact with Amazon SageMaker Unified Studio |
2025-07-22 21:38:48 |
| kedro-sagemaker |
0.4.0 |
Kedro plugin with AWS SageMaker Pipelines support |
2025-02-19 12:50:29 |
| exasol-sagemaker-extension |
0.11.3 |
Exasol SageMaker Integration |
2025-02-13 13:38:43 |
| fmbench |
2.1.2 |
Benchmark performance of **any Foundation Model (FM)** deployed on **any AWS Generative AI service**, be it **Amazon SageMaker**, **Amazon Bedrock**, **Amazon EKS**, or **Amazon EC2**. The FMs could be deployed on these platforms either directly through `FMbench`, or, if they are already deployed then also they could be benchmarked through the **Bring your own endpoint** mode supported by `FMBench`. |
2025-02-12 18:28:49 |
| sagemaker-studio-cli |
1.0.3 |
CLI to interact with SageMaker Studio |
2025-02-11 21:01:01 |
| inference-server |
1.3.2 |
Deploy your AI/ML model to Amazon SageMaker for Real-Time Inference and Batch Transform using your own Docker container image. |
2024-11-28 16:30:44 |
| gab-kedro-sagemaker |
0.3.0 |
Kedro plugin with AWS SageMaker Pipelines support |
2024-10-14 14:32:21 |