[TASK09] Improve Readme.MD

README.md

@@ -6,26 +6,9 @@ In this project, you will apply the skills you have acquired in this course to o
 
 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](https://www.kaggle.com/c/boston-housing). 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](https://kubernetes.io/), 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](https://review.udacity.com/#!/rubrics/2576/view).
-
-**The final implementation of the project will showcase your abilities to operationalize production microservices.**
-
----
-
 ## Setup the Environment
 
-* Create a virtualenv and activate it
+* Create a virtualenv and activate it. It's possible to create the virtual env with `make setup`.
 * Run `make install` to install the necessary dependencies
 
 ### Running `app.py`
@@ -40,3 +23,11 @@ You can find a detailed [project rubric, here](https://review.udacity.com/#!/rub
 * Setup and Configure Kubernetes locally
 * Create Flask app in Container
 * Run via kubectl
+
+## Files in this repo:
+
+The project has a few shell script files to help managing the resources.
+
+* **run_docker.sh**: builds a docker image and runs it, mapping the port 8000 of you computer.
+* **uploaddocker.sh**: tags and upload the docker image to a repo in docker hub. You may change the docker hub repository to meet one of your own.
+* **run_kubernets.sh**: deploy the application to a Kubernets node.