oracle-samples/oci-data-science-ai-samples
Mar 2025 – Mar 2025
1 Month active
Languages Used
HCLMarkdownPython
Technical Skills
CI/CDData ScienceInfrastructure as CodeMLOpsMachine LearningOCI
minovak
PROFILE
Minovak
Developed comprehensive examples for Oracle’s oci-data-science-ai-samples repository, focusing on packaging, deploying, and managing machine learning applications on Oracle Cloud Infrastructure. Leveraged Python and Terraform to create reusable infrastructure patterns and sample project structures, enabling reproducible and streamlined ML deployments. Integrated Infrastructure as Code principles to automate environment provisioning and documented clear CLI usage guidelines to support both onboarding and daily operations. The work emphasized MLOps best practices, CI/CD workflows, and practical guidance for using the ML Application CLI and OCI CLI, ultimately accelerating adoption and improving the maintainability of machine learning workloads within the Oracle Cloud ecosystem.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
7,813
Activity Months1
Your Network
815 peopleSame Organization
@oracle.com783
Aaron YoungMember
Carlos-CA-AlvarezMember
Dyre TjeldvollMember
grungtaMember
Kaiyuan LiMember
KarthikMember
Liam R. HowlettMember
Liam R. HowlettMember
Martin BalinMember
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 performance summary for oracle-samples/oci-data-science-ai-samples: Delivered OCI ML Application comprehensive examples with packaging, Terraform configurations, and CLI usage guidelines. Established reusable infrastructure patterns and sample project structures to accelerate ML deployment on OCI, improve reproducibility, and streamline onboarding for OCI ML workloads.
1 Commits • 1 Features
Mar 1, 2025March 2025 performance summary for oracle-samples/oci-data-science-ai-samples: Delivered OCI ML Application comprehensive examples with packaging, Terraform configurations, and CLI usage guidelines. Established reusable infrastructure patterns and sample project structures to accelerate ML deployment on OCI, improve reproducibility, and streamline onboarding for OCI ML workloads.
March 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
HCLMarkdownPython
Technical Skills
CI/CDData ScienceInfrastructure as CodeMLOpsMachine LearningOCIPythonTerraform
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