# Playbook

> Since 2021, Evil Martians has collaborated with Playbook to build a next-generation cloud platform for creatives that serves 700,000+ users worldwide. Our engineering teams have developed their core platform features – from ML-powered visual search and AI-assisted file management to Figma plugin and third-party integrations. Our relationship deepened when Playbook acquired CanFY, an AI assistant platform we incubated, bringing advanced image recognition and natural language processing capabilities to their creative ecosystem.

- URL: https://evilmartians.com/clients/playbook
- Industries: Business Tools, AI
- Services: Product development, Ruby on Rails consulting, Frontend & real-time, SDKs, extensions & plugins, Rails CI speedup
- Employees: 2–10

## Key facts

- **$22M+**: total funding

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Playbook, a platform for the modern creator, allows you to store, share and collaborate on visual content. Now with 50,000 active customers, they came to us for API optimizations, integrations, and to help develop a machine learning-powered search to simplify working with a large number of images.

## Serverless for machine learning
While building ML systems for the Playbook platform, we also helped optimize their architecture, making it as serverless as possible.

*Read also Evil Martians' article*: https://evilmartians.com/chronicles/serverless-typescript-a-complete-setup-for-aws-sam-lambda

For instance, we expanded their Google Cloud Functions capabilities, which they had already used earlier for the platform — this framework allowed them to develop features for analyzing and preprocessing images in JavaScript — and they utilized the [Serverless](https://www.serverless.com/) library for deployment. We began to work with small, optimized neural networks within the current architecture and were able to improve image processing. This approach was a big time saver since creating a new architecture would have required additional time and resources and would have complicated the overall infrastructure.

Watch this video “[Taking the edge off: demystifying edge computing using Cloudflare Workers](https://evilmartians.com/events/demystifying-edge-computing-using-cloudflare-workers-bites-and-bytes-lisbon)” by Alexander Baygeldin, Backend Engineer at Evil Martians. He uses Playbook as a perfect example of edge computing benefits we introduced in the project.

Further, we facilitated other cloud computing solutions. For example, when faced with the challenge of adding some system libraries to Cloud Functions, we implemented several services with the higher-level Google Cloud Run solution. This gave us the opportunity to prepare the container’s image, which runs on demand, on our own (in Cloud Functions, this image is predefined and unchangeable; you can only load and run your code there.) We also configured the deployment for cloud run through GitHub Actions.

The Evil Martians team also helped Playbook build a Figma plugin to easily browse assets from a Playbook account and import them into Figma projects as images.
*Read also Evil Martians' article*: https://evilmartians.com/chronicles/how-to-make-next-level-figma-plugins-auth-routing-storage-and-more

However, the limitations of Cloud Functions and Cloud Run do not allow graphics accelerators, and in addition, their instance types are quite limited in terms of resources. So, we needed to step up to an even higher level in the realm of serverless architecture. To work with heavier neural networks that require accelerators (GPUs), we started working with Managed Instance Groups. We took the opportunity to create an instance template that uses the accelerator, set up a startup script for it, and create a Managed Instance Group on top of it to run and shut down additional instances, depending on the load, in accordance with the auto-scaling settings.

By working with a variety of Google serverless tools, we managed to implement all the tasks related to building machine learning features without wasting time on setup and infrastructure support. Since all these tools have an auto-scaling feature and are designed to optimize computing resources, they save money and help withstand a sudden influx of users without the need for additional client actions.