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Enabling logging
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Application Insight
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Project workflow
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Comparing automl and hyperdrive model
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Deployment state
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Completed AutoML run
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Model deployment
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Details necessary for endpoint interaction
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AutoML run metrics
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Endpoint output
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Best hyperdrive model
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Endpoint response
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Deleting service after use
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Completed hyperdrive run
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Sample data to test endoint
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ONNX workflow
Inspiration
I have a strong background in banking and finance by having a degree in finance and working in the bank in the last couple of years. Over the years, I have also developed a passion in machine learning and AI. This here is my inspiration, finding myself in a field I have grown to love, combining knowledge of lending systems and this new machine learning passion. This project is about building intelligent tools that solve real-world challenges in financial institutions, one major one is loan default prediction.
What it does
The project identifies clients that are likely to default a loan credit.
How we built it
Project was built by training machine learning models on Azure ecosystem; leveraging tools such as automl, hyperparemeter tuning with hyperdrive, MLOps with pipelines, etc.
Challenges we ran into
Data privacy. Letting out clients information for the project was a challenge. A little simulation/editing of data was done to prevent valuable clients information from being released to the public.
Accomplishments that we're proud of
Project hit an accuracy of about 85% with AutoML. Model was deployed and consumed by exposing its RESTful endpoint API. Model was also converted to ONNX format, so it can be used on android and iOS devices
What we learned
There are so many resources on Azure to applicable to machine learning tasks.
What's next for Loan Default Prediction
Push it to the company's executive and see if it can be implemented in operations.
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