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You can read more about the data, which was initially taken from Kaggle, on [the data source site](https://www.kaggle.com/c/boston-housing). 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.\n\n### Project Tasks\n\nYour project goal is to operationalize this working, machine learning microservice using [kubernetes](https://kubernetes.io/), which is an open-source system for automating the management of containerized applications. 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Run in Kubernetes:  `./run_kubernetes.sh`\n\n### Kubernetes Steps\n\n* Setup and Configure Docker locally\n* Setup and Configure Kubernetes locally\n* Create Flask app in Container\n* Run via kubectl\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwaelson%2Fmachine-learning-microservice-api","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwaelson%2Fmachine-learning-microservice-api","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwaelson%2Fmachine-learning-microservice-api/lists"}