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Project Overview

In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API.

You are given 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. You can read more about the data, which was initially taken from Kaggle, on the data source site. 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.

Setup the Environment

• Create an environment: virtualenv env_devops
• Set your environment: source env_devops/bin/activate
• Install dependencies: make install or pip install -r requirements.txt

Running app.py

• Run app: python app.py
• Run in Docker:
  1. Install Docker
  2. Execute the script: sh ./run_docker.sh
  3. Application will be expose on port 80
• Run in Kubernetes:
  1. Install or activate MiniKube
  2. Execute the script: sh ./run_kubernetes.sh
  3. Application will be expose on port 80

Testing Housing Pricing Model

After application is running you can do calls to the housing pricing model with the next script:

sh ./make_prediction.sh