https://github.com/emmanuel-dominic/devops-microservices-kubernetes
Project having a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. This project tests your ability to operationalize a Python flask app in a provided file, `app.py` that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.
https://github.com/emmanuel-dominic/devops-microservices-kubernetes
ci-cd circleci data-analysis-python devops docker kubernetes microservices
Last synced: 6 months ago
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Project having a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. This project tests your ability to operationalize a Python flask app in a provided file, `app.py` that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.
- Host: GitHub
- URL: https://github.com/emmanuel-dominic/devops-microservices-kubernetes
- Owner: Emmanuel-Dominic
- Created: 2022-06-16T16:51:51.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-16T16:51:52.000Z (over 3 years ago)
- Last Synced: 2025-03-14T10:44:30.033Z (about 1 year ago)
- Topics: ci-cd, circleci, data-analysis-python, devops, docker, kubernetes, microservices
- Homepage:
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# devops-microservices-kubernetes
Project having a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. This project tests your ability to operationalize a Python flask app in a provided file, `app.py` that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.