May 4th Community Call — Webinar Recap
Tags: serverless, python, machinelearning, aws
Published: 2022-05-10 16:07:52

[Watch here!](https://app.livestorm.co/serverless/serverless-community-call-may-2022?type=detailed&utm_source=Devto&utm_medium=post&utm_campaign=Blog_Recap&utm_id=Community+Call)

Originally posted at [Serverless](https://www.serverless.com/blog/may-4th-community-call-webinar-recap)

![](https://cdn-images-1.medium.com/max/3200/0*cOZFIHyMgfmqBgyS.png)

**The on-demand recap of the May 2022 Serverless Community Call on May 4th is now available!**

[Watch here!](https://app.livestorm.co/serverless/serverless-community-call-may-2022?type=detailed&utm_source=Devto&utm_medium=post&utm_campaign=Blog_Recap&utm_id=Community+Call)

We had a packed agenda, showing off big product updates — Serverless Framework Compose, early access to Serverless Console, and a user story of how WAIR uses Serverless to help brands like Nike help shoppers fit clothes online.

## Serverless Framework Compose

We were excited to announce [Serverless Framework Compose](https://www.serverless.com/blog/serverless-framework-compose-multi-service-deployments): a new feature enabling you to deploy multiple services in one command, in parallel, or ordered by dependencies!

With Compose, it is possible to deploy multiple Framework services via a single serverless deploy command with aserverless-compose.yml file.

To use this feature, [upgrade the serverless CLI](https://www.serverless.com/framework/docs/getting-started#upgrade) to v3.14 or greater and read [Matthieu Napoli’s guide on how to get started](https://www.serverless.com/framework/docs/guides/compose).

## WAIR’s Serverless Story

Tyler Carter, CTO and co-founder of [WAIR](https://clr3p04.na1.hubspotlinks.com/Ctc/DJ+113/clr3p04/VVtfjX4n9KqSW6-1Wh-4QBp13W7Y8zfc4J81g2N26s60p3q905V1-WJV7CgLDxW7p2X1V8k54RZW3z9t782dQs1fW7g4v-78xqlL6VtQ82g7ytnqTW12Cq5v8zhFZ1W27xhGJ6BnfWqW5GMBtd2vt6tbW8ZvMJs8PKghRW7j9M7V6jVDpFN8dPWDGcS9vjW8t8NXP96dm2LW2hVL8J4N92m0W3QqXNt5CX4YyW4qkk2K5JrhCmW7wNYrc5cJympW95GR0y3F9wzmN5JHpBNDzG-MW4pHqmY7LqH_fW75wBtS8Y9rPfW6xByQM4H5CqSW3b8ppc1Nj05YW3KD3cd3fsfMfN1s2S57pPNNZW69RR782zfNZd3mz71) and [Fit3D](https://clr3p04.na1.hubspotlinks.com/Ctc/DJ+113/clr3p04/VVtfjX4n9KqSW6-1Wh-4QBp13W7Y8zfc4J81g2N26s60p3q905V1-WJV7CgMVLW4TCNGr6ydhYyW7c-Zg38W6B9-W677jJX81xh1FN1M2HL52kZ6dW8SzGh4577wzlW93hd9k2xsTkzW1McKbS2b10KLW83gMf_8b8yF5W8kv16y7cmz81W8d9nF353wM4SW5zV4FY3nrCQfW2vHXDj40Xk2WW3Ccxp-6VDKzdW3TYs6P4DB6WKW35FX6f3p7ttRN8XnR-bxQNR2W3bxYWV7HZqxMW57J9Tl3sClxmW2YZT007pgN9dN7ldd5vnkhkyW6zjdnx8VH_g8W3tS1xT13qW5gN4DGMCc4j4q-VPJGyN8k-2CY3c0x1), shared their Serverless journey. WAIR helps shoppers order the best fitting clothing for brands including Nike using the world’s largest 3D body dataset.

Tyler shared how Serverless helps them dynamically scale millions of requests for events like Black Friday, and use the Framework to create APIs and power machine learning (ML) Python workloads with AWS Lambda, Amazon Sagemaker, and containers.

Connect with Tyler:

* [Twitter](https://twitter.com/thcarter_)

* [WAIR on Twitter](https://twitter.com/getwair)

* [LinkedIn](https://www.linkedin.com/in/tcarter10/)

*Originally published at [https://www.serverless.com](https://www.serverless.com/blog/may-4th-community-call-webinar-recap).*