google-ai-edge/mediapipe-samples
May 2025 – May 2025
1 Month active
Languages Used
Objective-CSwift
Technical Skills
Dependency ManagementMachine Learning InferenceiOS Development
Prianka Liz Kariat
PROFILE
Prianka Liz Kariat
Prianka Kariat worked on stabilizing and enhancing the google-ai-edge/mediapipe-samples repository, focusing on production-ready deployment for machine learning inference on iOS. She updated dependency management by aligning Podfile.lock and CocoaPods with the latest MediaPipeTasksGenAI libraries, reducing build fragility and ensuring compatibility. Prianka corrected the Gemma 3 model path to enable reliable model loading and tuned inference parameters for the Gemma 2 model, improving output relevance in production demos. Her work, primarily in Swift and Objective-C, addressed both feature development and bug fixes, demonstrating a solid understanding of dependency management and machine learning integration within iOS development workflows.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
21
Activity Months1
Your Network
21 peopleWork History
May 2025
3 Commits • 2 Features
May 1, 2025In May 2025, focused on stabilizing and enhancing the google-ai-edge/mediapipe-samples repository to support reliable, production-ready deployments. Key work includes dependency updates to ensure compatibility with the latest MediaPipe libraries (MediaPipeTasksGenAI/MediaPipeTasksGenAIC); precise fixes to Gemma model loading; and targeted tuning of Gemma 2 inference for more relevant outputs. These efforts reduce build fragility, improve runtime reliability, and deliver more accurate inferences in production demos.
3 Commits • 2 Features
May 1, 2025In May 2025, focused on stabilizing and enhancing the google-ai-edge/mediapipe-samples repository to support reliable, production-ready deployments. Key work includes dependency updates to ensure compatibility with the latest MediaPipe libraries (MediaPipeTasksGenAI/MediaPipeTasksGenAIC); precise fixes to Gemma model loading; and targeted tuning of Gemma 2 inference for more relevant outputs. These efforts reduce build fragility, improve runtime reliability, and deliver more accurate inferences in production demos.
May 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Objective-CSwift
Technical Skills
Dependency ManagementMachine Learning InferenceiOS Development
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