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
Worked on stabilizing and enhancing the google-ai-edge/mediapipe-samples repository to support production-ready machine learning inference on iOS. Addressed dependency management by updating Podfile.lock and upgrading CocoaPods, ensuring compatibility with the latest MediaPipeTasksGenAI libraries. Improved model reliability by correcting the Gemma 3 model path and asset references, resolving issues with model loading. Tuned inference parameters for the Gemma 2 model, refining temperature, topK, and topP settings to yield more relevant outputs. Leveraged Objective-C and Swift to implement these changes, reducing build fragility and improving runtime reliability for production demos that showcase machine learning capabilities on Apple platforms.
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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