devops

[](https://circleci.com/gh/smita-hub/devops) ### Project Overview In this project(Microservices at scale using AWS and Kubernetes), I have applied the skills I have acquired in this course to operationalize a Machine Learning Microservice API and I have used AWS cloud9 IDE for all my project work. There is 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. To read more about the data, which was initially taken from Kaggle, on [the data source site](https://www.kaggle.com/c/boston-housing). This project tests the 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 The project goal is to operationalize this working, machine learning microservice using [kubernetes](https://kubernetes.io/), which is an open-source system for automating the management of containerized applications. In this project below concepts are applied: * 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 ## Setup the Environment * Create a virtualenv and activate it. * Run `make install` to install the necessary dependencies required for running the application like Hadoling and from requirements.txt ### Running `app.py` in different cases. 1. Standalone in my local: `python app.py` 2. To run in Docker: `./run_docker.sh` 3. To run in Kubernetes: `./run_kubernetes.sh` ### Kubernetes Steps * Setup and Configure Docker locally * Setup and Configure Kubernetes locally * Setup and Configure Minikube locally * Create Flask app in Container * Run via kubectl