Azure/azureml-assets
May 2026 – May 2026
1 Month active
Languages Used
DockerfilePythonYAML
Technical Skills
Azure MLData ScienceDependency ManagementDevOpsDockerMachine Learning
Sid Kiblawi
PROFILE
Sid Kiblawi
Worked on the Azure/azureml-assets repository to enhance the MIP-3D training stack by delivering a secure, up-to-date base image and introducing a new MedImageParse 3D finetune component. Focused on stability and security, the work involved hardening Dockerfiles and Python dependencies, performing CVE remediation, and reducing vulnerabilities in the training environment. Leveraged Azure ML, Docker, and Python to validate end-to-end training workflows, ensuring compatibility and reliability for volumetric medical image processing. The updates streamlined dependency management, stabilized import chains, and accelerated future patching, resulting in a more robust and compliant machine learning pipeline for medical imaging applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
610
Activity Months1
Your Network
5041 peopleSame Organization
@microsoft.com4977
GitOpsMember
Ananta GuptaMember
Abi GicicMember
Abigail HartmanMember
Abram SandersonMember
Adam EttenbergerMember
Adam KrantzMember
Alexandre GattikerMember
Ami HollanderMember
Work History
May 2026
3 Commits • 2 Features
May 1, 2026May 2026 monthly summary: Focused on stability, security, and new capabilities for the MIP-3D training stack in AzureML assets. Delivered a secure, up-to-date base image for MIP-3D finetune, implemented extensive dependency & image hardening, added a MedImageParse 3D finetune component, and validated end-to-end training workflows on AML with the rebuilt image. The work reduces risk, improves training reliability, and accelerates future patching.
3 Commits • 2 Features
May 1, 2026May 2026 monthly summary: Focused on stability, security, and new capabilities for the MIP-3D training stack in AzureML assets. Delivered a secure, up-to-date base image for MIP-3D finetune, implemented extensive dependency & image hardening, added a MedImageParse 3D finetune component, and validated end-to-end training workflows on AML with the rebuilt image. The work reduces risk, improves training reliability, and accelerates future patching.
May 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture93.4%
Performance80.0%
AI Usage53.4%
Skills & Technologies
Programming Languages
DockerfilePythonYAML
Technical Skills
Azure MLData ScienceDependency ManagementDevOpsDockerMachine LearningPython DevelopmentSecurity Compliance
Repositories Contributed To
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