aws-samples/sagemaker-genai-hosting-examples
May 2026 – May 2026
1 Month active
Languages Used
Python
Technical Skills
AWSMachine LearningOpenAIPythonSageMaker
Marc Karp
PROFILE
Marc Karp
Developed a SageMaker Real-time Inference Deployment and Invocation Notebook for the aws-samples/sagemaker-genai-hosting-examples repository, focusing on end-to-end real-time inference workflows using the OpenAI SDK. The solution demonstrated deploying single-model endpoints, creating inference components, and orchestrating multi-agent workflows with Strands Agents, all within a Jupyter notebook environment. Implemented bearer token authentication with auto-refreshing tokens to support production-grade security and reliability. Leveraged AWS, Python, and SageMaker to accelerate production readiness for real-time inference, enabling secure, scalable deployment patterns. The work reduced time-to-value for experimentation and rollout, providing reusable patterns for robust machine learning inference in cloud environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
866
Activity Months1
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Work History
May 2026
1 Commits • 1 Features
May 1, 2026Month: 2026-05 — Key features delivered: SageMaker Real-time Inference Deployment and Invocation Notebook in aws-samples/sagemaker-genai-hosting-examples. This Jupyter notebook demonstrates end-to-end real-time inference using the OpenAI SDK, covering bearer-token authentication, deploying single-model endpoints, creating inference components, and building Strands Agents multi-agent workflows; includes auto-refreshing token authentication for production use. Major bugs fixed: No major bugs fixed reported this period. Overall impact and accomplishments: Accelerated production readiness for real-time SageMaker deployments using OpenAI SDK, enabling secure, scalable inference patterns and multi-agent orchestration, reducing time to experiment and roll out changes. Technologies/skills demonstrated: AWS SageMaker real-time inference, OpenAI SDK, Jupyter notebooks, bearer token authentication, single-model endpoints, inference components, Strands Agents multi-agent workflows, auto-refreshing token authentication. Commit: 4b6b2fb389ad02c220168664d21f431bc6aa8bff.
1 Commits • 1 Features
May 1, 2026Month: 2026-05 — Key features delivered: SageMaker Real-time Inference Deployment and Invocation Notebook in aws-samples/sagemaker-genai-hosting-examples. This Jupyter notebook demonstrates end-to-end real-time inference using the OpenAI SDK, covering bearer-token authentication, deploying single-model endpoints, creating inference components, and building Strands Agents multi-agent workflows; includes auto-refreshing token authentication for production use. Major bugs fixed: No major bugs fixed reported this period. Overall impact and accomplishments: Accelerated production readiness for real-time SageMaker deployments using OpenAI SDK, enabling secure, scalable inference patterns and multi-agent orchestration, reducing time to experiment and roll out changes. Technologies/skills demonstrated: AWS SageMaker real-time inference, OpenAI SDK, Jupyter notebooks, bearer token authentication, single-model endpoints, inference components, Strands Agents multi-agent workflows, auto-refreshing token authentication. Commit: 4b6b2fb389ad02c220168664d21f431bc6aa8bff.
May 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AWSMachine LearningOpenAIPythonSageMaker
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