project-ml-microservices-kubernotes

Project Overview

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.

Project Tasks

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.

Steps (Without Docker)

Create a virtual environment and activate it:
```
$ make setup
```
Install all the dependencies:
```
 (.devops) $ make install
```
Start the webapp
```
 (.devops) $ python app.py
```

Steps (With Docker)

Install Docker.
Run the bash script:
```
 $ bash run_docker.sh
```

Steps (With Kubernetes)

Install Minikube.
Start Minikube:
```
 $ minikube start
```
Run the bash script:
```
 bash run_kubernetes.sh
```

Files

docker_out.txt: Logs generated when the webapp is deployed with docker.
kubernetes_out.txt: Logs generated when the webapp is deployed with kubernetes.
make_prediction.sh: Bash script to make POST request to localhost:8000 to make prediction with sample input.
run_kubernetes.sh: Bash script to start the webapp with kubernetes.
run_docker.sh: Bash script to start the webapp with docker.
upload_docker.sh: Bash script to upload docker image to docker hub.

dojutsu-user / project-ml-microservices-kubernotes