End to End Machine Learning with Python in IBM Watson Studio:

Hacker News ML Viewer: Building an ML powered application

Prerequisites

Data processing and machine learning

• Jupyter Notebooks - code/data IDE
  • Using IBM Watson Studio notebooksfor this application for easy scheduling and model deployment
  • Recommend downloading Anaconda for local development, includes notebooks.
• Object storage - data persistence
  • This is a very simple app just using IBM Cloud object storage for managing data
  • This app can easily be upgraded to a NoSQL document store DB like Cloudant

Application Development

• Text editor of your choice (I like Atom)
• Python environment capable of running Flask (think Anaconda install will cover this dependency)

Deploy to IBM Cloud

If you want to deploy your app to the IBM Cloud, you'll need the following:

• IBM Cloud account
• Cloud Foundry CLI
• Git
• Python

1. Clone this repository

Now you're ready to start working with the app. Clone the repo and change to the directory where the sample app is located.

git clone https://github.com/gfilla/pycon2018
cd pycon2018

Peruse the files in the pycon2018 directory to familiarize yourself with the contents.

2. Run the app locally

Install the dependencies listed in the requirements.txt file to be able to run the app locally.

You can optionally use a virtual environment to avoid having these dependencies clash with those of other Python projects or your operating system.

pip install -r requirements.txt

Run the app.

python app.py

View your app at: http://localhost:8000

3. Review stages of app development

1. Requirements gathering
2. Data Collection
3. Machine Learning - Model training / experimentation
4. Flask application Development
5. Model deployment and management

See these slides for more details

4. Prepare the app for deployment

To deploy to IBM Cloud, it can be helpful to set up a manifest.yml file. One is provided for you with the sample. Take a moment to look at it.

The manifest.yml includes basic information about your app, such as the name, how much memory to allocate for each instance and the route. In this manifest.yml random-route: true generates a random route for your app to prevent your route from colliding with others. You can replace random-route: true with host: myChosenHostName, supplying a host name of your choice. Learn more...

applications:
- name: pycon2018
  random-route: true
  memory: 128M

5. Deploy the app

You can use the Cloud Foundry CLI to deploy apps.

Choose your API endpoint

cf api <API-endpoint>

Replace the API-endpoint in the command with an API endpoint from the following list.

URL	Region
https://api.ng.bluemix.net	US South
https://api.eu-de.bluemix.net	Germany
https://api.eu-gb.bluemix.net	United Kingdom
https://api.au-syd.bluemix.net	Sydney

Login to your IBM Cloud account

cf login

From within the pycon2018 directory push your app to IBM Cloud

cf push

This can take a minute. If there is an error in the deployment process you can use the command cf logs <Your-App-Name> --recent to troubleshoot.

When deployment completes you should see a message indicating that your app is running. View your app at the URL listed in the output of the push command. You can also issue the

cf apps

command to view your apps status and see the URL.