PROJECT_NAME:operationalize a Machine Learning Microservice API VCS: GitHub LINK: https://circleci.com/gh/kent5i5/dockerproject/tree/master.svg?style=svg
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
-
./cicleci/config.yml - a config file used to set up my dockerfile project in circleci connecting my GitHub docker project repository.
-
Makefile - contains all make commands that used to create the python environment and linting the application.
-
app.py - the application used to do the prediction
-
requirement.txt - list of python add-on required, make command use this file to install the require app for the environment.
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run_docker.sh - automated script used to create docker image and run the app after it is created.
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upload_docker.sh - upload the image created from run_docker.sh and put it into docker hub repo