Operationalize a Machine Learning Microservice API

Udacity Cloud DevOps - Microservices at Scaling using AWS & Kubernetes

Project Overview

The project uses a pre-trained, sklearn model with Python flask app to operationalize a Machine Learning Microservice API.

And, covers the following tasks:

• Test project code using linting
• Complete a Dockerfile to containerize this application
• Deploy 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

How to run

• Building a virtual environment

python3 -m venv ~/.devops
source ~/.devops/bin/activate

• Completing docker file using lint test

cat Dockerfile
cat Makefile
make install
make lint

• Running Docker container and call prediction API

./run_docket.sh

# Open new terminal, call to prediction API
./make_prediction.sh
# Output logs are pasted in `docker_out.txt`

• See logs from prediction API

cat docker_out.txt

• Uploading Docker image

./upload_docker.sh

• Configuring Kubernetes

minikube start
kubectl config view

• Deploying with kubernetes

./run_kubernetes.sh

# Open new terminal, call to prediction API
./make_prediction.sh
# Output logs are pasted in `kubernetes_out.txt`

• Integrated on CircleCI and status badge is added.

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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

You can find a detailed project rubric, here.

The final implementation of the project will showcase your abilities to operationalize production microservices.

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Setup the Environment

• 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

Running app.py

1. Standalone: python app.py
2. Run in Docker: ./run_docker.sh
3. Run in Kubernetes: ./run_kubernetes.sh

Kubernetes Steps

• Setup and Configure Docker locally
• Setup and Configure Kubernetes locally
• Create Flask app in Container
• Run via kubectl