MongoDB Atlas & GCP AI/ML

Background

This is a tutorial on how to use MongoDB Atlas in conjunction with Google's AI & Machine Learning Products to create an event-driven model in Python.

This is part of a workshop series presented by MongoDB and Google Cloud, however it can also be followed along on its own. The Google aspects of the workshop are based on Google's Vision AI.

Technical Complexity

Beginner to Intermediate

Technology

Familiarity with the following technologies will be of benefit:

Python 3
HTML
Basic JSON (will be covered in MongoDB introduction slides)

Software Requirements

Modern web browser such as Google Chrome (Required to access MongoDB Atlas, GCP Console, and Google CloudShell)
Optionally install MongoDB Compass, the GUI for MongoDB

Duration

45 Minutes

Setup

High Level Readme

Setup a MongoDB Atlas Account
Deploy a free-tier MongoDB Atlas M0 cluster
Create a database username/password and setup a suitable IP whitelist entry to allow access to the cluster
Make note of the connection string for Python
Create a Google Cloud account if required
Enable the Cloud Vision API
Create a service credential and download the JSON file
Create a GCP instance
Open firewall rules in GCP for ports 8088 and 8089 for this instance
Install python and clone this github repo on the GCP instance
Configure the FinishedSampleCode/settings.cfg and FinishedSampleCode/gcpcreds.cfg with the Atlas connection string and GCP credentials that you created earlier
Install any python requirements by running pip install -r FinishedSampleCode/requirements.txt
Start the application using python3 FinishedSampleCode/runner.py
Open a web browser to the GCP instance running on port 8088 over http: http://<host>:8088

Low Level Readme

Note: You can choose between using pre-written code, which is linked/cloned in the 'Readme for configuring the GCP instace' section (3), or to write the code from scratch, following along in the section (4) that follows

Execution

Start the application using python3 FinishedSampleCode/runner.py
Open a web browser to the GCP instance running on port 8088 over http
Insert a document into the database using MongoDB Compass or the new Data Explorer view in MongoDB Atlas which you can get to via the "Collections" button. The document you insert should have a field called url which is a full URL to an image.
Alternately enter the URL into the web page directly and press the green "Insert" button
Notice that in the web page and on the CLI output of the python script that it saw an insert
Notice that after the insert, the change stream called the Google Vision API to see what is in it. Refresh the Compass or Atlas Data Explorer view and see the rich data structure of the GCP Vision API.

Sample Data

Feel free to use the following URLs for execution:

Extra Credit

Create a GCP Cloud Storage Bucket to host your own images
Follow along with the runnerAdvanced.py using the GCP Natural Language API to do sentiment analysis on non-URL text in the text attribute of a document
- When the runnerAdvanced.py script is running you can enter text into the search box of the web application and the code will perform sentiment analysis on it, presenting the results in the output pane
- Note: you will need to enable the 'Cloud Natural Language API' in your GCP project before running the runnerAdvanced.py script, in the same way you enabled the 'Cloud Vision API'
Find another GCP API and integrate with it. Examples:
- Use the Cloud Translation API to extend the runnerAdvanced.py to translate your text into another language
- Consider integrating the Google Assistant API

Tear Down

Once you're ready to terminate the project you can tear down each component as follows:

Remove the GCP Firewall rule
Terminate the GCP VM
Remove the GCP "Service Account Key" Credentials
Disable the GCP Cloud Vision API and (if required) the Cloud Natural Language API
Shutdown the GCP Project
Remove the MongoDB Atlas Whitelist rule
Remove the MongoDB Atlas user
Remove the MongoDB Atlas cluster

flaminscotsman/MongoDBAtlas-GCP-AIMLv2