Joot & Serverless: Machine Learning for Image Optimization on the Serverless Framework

May 4, 2022

Joot + Serverless

Joot, a social media image optimization tool, needed to accelerate their development and deploy their application quickly. Serverless was an all-in-one solution for Joot. They were able to get up and running quickly with the necessary flexibility to meet a vast array of scenarios. With the help of Serverless, Joot, in a 6 month period of time was able to:

  • Complete the development of its visual AI software. 
  • Successfully improved ROI (return on investment) for their key customers.
  • Provide previously unavailable insights into their customer’s social media efforts.
  • Create a competitive advantage for their clients and help achieve the highest possible results. 

Key benefits of going Serverless

Some of the key technical benefits for Joot in choosing to use Serverless were:

  • Shortest path to architecture, development and deployment.
  • Complete (and ever-growing) suite of web-services to fit nearly any requirement.
  • Flexible development environment supporting multiple languages.  Joot uses both Node.js and Python for their machine learning work .
  • Inherently scalable by design with drastically reduced resource-management requirements.
  • Low cost environment to develop, test, and experiment.. 

Joot’s image processing architecture can take up to 12 different steps in their processing pipeline, and some functions can run several minutes due to machine learning processing and model creation.  Serverless Framework made it easy to orchestrate those steps.

Unlike containers or virtual machines, going Serverless let Joot pay for only the resources they actually consumed and let them stage and tear down entire environments within minutes.  The Serverless Framework streamlined everything - from development to deployment and operations, enabling them to create functioning web services within minutes and define their architecture in code.  It also radically simplified duplication of their local dev environments to match what was in production.

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