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.
Your 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 your 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
- Create a free docker account, where you'll choose a unique username and link your email to a docker account. Your username is your unique docker ID.
- To install the latest version of docker, choose the Community Edition for your operating system, on docker's installation site.
- After installation, you can verify that you've successfully installed docker by printing its version in your terminal:
docker --version
- Setup and Configure Kubernetes locally
- Install a virtual machine like VirtualBox:
For Mac:
brew cask install virtualbox
For Windows: recommend to use a Windows host - Install minikube :
For Mac :
brew cask install minikube
For Windows: recommend using Windows installer - Run
minikube start
- Check if cluster is running
kubectl config view
-
Create Flask app in Container Run the docker sript file:
./run_docker.sh
Upload the built image:./upload_docker.sh
-
Run via kubectl Run the kubernetes script file:
./run_kubernetes.sh