The project aims to operationalize a Machine Learning Microservice API. It contains a pre-trained, sklearn model that is trained to predict prices of houses in Boston based on average rooms in a home,data about highway access, teacher-to-pupil ratios, and so on. More information on the data source site.The main aim is to tests the operationalization a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls. The project can be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.

1. run_docker.sh - contains scripts that will run snd build docker image
2. upload_docker.sh -contains scripts that will upload docker image to the registry
3. make_prediction.sh - contains scripts that will allow predictions
4. run_kubernetes.sh - contains scripts that will run the app in the kubernetes pod in minikube
5. hadolint.sh - contains scripts that will allow installing of hadolint
6. Makefile -contains steps that will initializing project setup

1. Activate the virtualenv before anything is installed

• Create a virtualenv with Python 3.7 and activate it. Refer to this link for help on specifying the Python version in the virtualenv.

python3 -m pip install --user virtualenv
# You should have Python 3.7 available in your host. 
# Check the Python path using `which python3`
# Use a command similar to this one:
python3 -m virtualenv --python=<path-to-Python3.7> ~/.mlapp
source ~/.mlapp/bin/activate

2. Run make install to install the necessary dependencies

3. Inorder to run app.py you need to do the following in different environments i. Standalone: python app.py ii. Run in Docker: ./run_docker.sh ii. Run in Kubernetes: ./run_kubernetes.sh

• Setup and Configure Docker locally link: Docker Install
• Setup and Configure Kubernetes via minikube locally link: Kubernetes Install
• Create Flask app in Container
• Run via kubectl