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.
- 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> .devops
source .devops/bin/activate- Run
make installto install the necessary dependencies
- Standalone:
python app.py - Run in Docker:
./run_docker.sh - Run in Kubernetes:
./run_kubernetes.sh
- Setup and Configure Docker locally
- Setup and Configure Kubernetes locally
- Create Flask app in Container
- Run via kubectl.
- run_docker.sh - Files to build and run docker image
- upload_docker.sh - Files to upload images to docker hub
- run_kubernetes.sh - Files to deploy to kubernetes
- Makefile - Files to build application
- app.py - Application file
- requirements.txt - Python dependencies to run the app
- output_txt_files/docker_out.txt - log output from Docker prediction
- output_txt_files/kubernetes_out.txt - log output from Kubernetes-mediated prediction
- model_data/ - Folder for Application Models
- .circleci/ - Folder for Circleci Config File