In this project, I have applied the skills acquired in this course to operationalize a Machine Learning Microservice API.
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 my 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.
The project goal is to operationalize this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. In this project you will:
- Test project code using linting
- Complete a Dockerfile to containerize this application
- Deploy your containerized application using Docker and make a prediction
- Improve the log statements in the source code for this application
- Configure Kubernetes and create a Kubernetes cluster
- Deploy a container using Kubernetes and make a prediction
- Upload a complete Github repo with CircleCI to indicate that your code has been tested
You can find a detailed project rubric, here.
The final implementation of the project will showcase your abilities to operationalize production microservices.
- 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 install
to 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
kubectl run machine-learning-api --image=$dockerpath
.circleci/config.yml - circleci configuration
model_data - housing prices in Boston area
output_files/docker_out.txt - docker log outputs
output_files/kubernetes_out.txt - kubernetes log outputs
app.py - flask app API endpoint with routes to get house prices in Boston
Dockerfile - Docker configuration file
make_prediction.sh - script to log predictions endpoint output
Makefile - The Makefile includes instructions on environment setup and lint tests
requirements.txt - python dependencies for this project
run_docker.sh - shell script to build docker image and run it
run_kubernetes.sh - shell script to run the Docker Hub container with kubernetes
upload_docker.sh - shell script to upload local docker build image to docker hub (online repository)