Uploaded March 2022 | Updated September 2026, 2 weeks ago

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This is the second part of developing machine learning (ML) project from zero to final deployment into the production. With this video we will deploy a pre-built Flask application to production environment with AWS Beanstalk.

AWS Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS. Read more here: aws.amazon.com/elasticbeanstalk

There are many other solutions how you can deploy you ML model into the production, such as using Kubernetes with Docker, SageMaker, or use native solutions from other cloud providers (Google Cloud, Azure, or others). Why I choose AWS Beanstalk? - Because it is very fast, complete resource control, integration with Git and much more by side. However, it is not free, you would be billed with some invoices which covers your deployment infrastructure.

So, if you want to take a look how your Flask application will look like in production without doing many manual steps and time-consuming configuration, follow the video.

The content of the tutorial is:
0:00 - Step 1. Make a local application test
0:54 - Step 2. Specify app. dependencies
3:19 - Step 3. Build a Virtual environment
5:36 - Step 4. Adapt the application code
7:11 - Step 5. Add configuration file in .ebextensions
8:43 - Step 6. Zip your Flask application
9:26 - Step 7. Deploy application to AWS Beanstalk
11:38 - Test the final application in the cloud

Also, in this video you will learn:
- How to create a virtual environment (Mac)
- Find correct versions of dependencies with pip list command (2:25)
- Install dependencies from requirements.txt file (4:00)
- How to prepare a Flask code based on AWS Beanstalk requirements
- How to handle .DS_Store files in Mac laptops with ignoring them while zipping
- How to zip files with terminal command in Mac.
- How to specify a custom URL for your web application

Quick tips of the video:
- Each dependency can cover it's own deeper dependencies (you can find it with pip freeze command - 4:55)
- AWS Beanstalk requires "application" instead of "app"
- python.config configuration file is available here: github.com/vb100/KMeans-FlaskApp/blob/master/.ebextensions/python.config
- Python virtual environment is being created in AWS Beanstalk directly so we do not need to import it manually from a local machine.

The video tutorial of the Part #1: K-Means Python script to Flask application (youtu.be/HC7fdYyAcUc)

#beanstalk #flask #machinelearning

Thank you for watching!