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 and activate it
- 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
app.py
- The python application that returns predictionsDockerfile
-Dockerfile
used to build Docker imageMakefile
- Provides sets of instructions to be run with themake
commandmake_prediction.sh
- Script to quickly post data to the applicationmodel_data/
- Provided with the project. Training data?output_text_files/
- Output examplesdocker_out.txt
- Sample output from application running in Dockerkubernetes_out.txt
- Sample Kubernetes output
README.md
- This filerequirements.txt
- A list of Python dependencies to be installed usingmake install
run_docker.sh
- Script to run the application locally in Dockerrun_kubernetes.sh
- Script to run the application in a Minikube deploymentupload_docker.sh
- Script to upload Docker container to Docker Hub (must be run afterrun_docker.sh