IBM/watsonx-ai-samples
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSSHTMLPython
Technical Skills
Data ScienceIBM WatsonJupyter NotebookMachine LearningONNXPython
Dorota Lączak
PROFILE
Dorota Lączak
Developed an end-to-end TensorFlow-to-ONNX conversion workflow within the IBM/watsonx-ai-samples repository, enabling seamless integration with IBM Watson Machine Learning. The solution, implemented as a Jupyter Notebook using Python and ONNX, guides users through downloading models, converting them to ONNX format, and deploying them for scoring, thereby reducing manual intervention and streamlining production deployment. The work also expanded support for both TensorFlow and PyTorch models, enhancing cross-framework interoperability and reuse across teams. This contribution demonstrated proficiency in data science and machine learning workflows, focusing on practical deployment challenges and accelerating model integration within Watson ML pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
28,965
Activity Months1
Your Network
9 peopleWork History
November 2024
1 Commits • 1 Features
Nov 1, 2024Delivered an end-to-end TensorFlow-to-ONNX conversion notebook within IBM/watsonx-ai-samples to enable seamless IBM Watson Machine Learning integration. The workflow covers model download, ONNX conversion, and deployment for scoring, reducing manual steps and accelerating productionization of ML models in Watson ML pipelines. The work also includes expanded multi-framework support (PyTorch and TensorFlow) via additional notebooks, broadening interoperability and reuse across teams.
1 Commits • 1 Features
Nov 1, 2024Delivered an end-to-end TensorFlow-to-ONNX conversion notebook within IBM/watsonx-ai-samples to enable seamless IBM Watson Machine Learning integration. The workflow covers model download, ONNX conversion, and deployment for scoring, reducing manual steps and accelerating productionization of ML models in Watson ML pipelines. The work also includes expanded multi-framework support (PyTorch and TensorFlow) via additional notebooks, broadening interoperability and reuse across teams.
November 2024
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
CSSHTMLPython
Technical Skills
Data ScienceIBM WatsonJupyter NotebookMachine LearningONNXPythonTensorFlow
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