Effortlessly serve your Python functions through a managed web server.

We are launching fal-serverless, a cloud based Python runtime that simplifies infrastructure for teams who are building data pipelines, analytics, ML training and inference.

We are excited to bring you our second Launch Week! Over the past couple of months we have been working on exciting new features to make running Python even more delightful. Similar to the previous launch week [https://blog.fal.ai/announcing-fal-launch-week-1/], each day of the week we are going

Python vs SQL is a frequent debate in the data community and it usually revolves around which language is better for data transformations. At Features and Labels [https://fal.ai/], we think about it a little differently. In our opinion, today, all transformations that are supported by SQL should be

We started the fal project with a simple objective; we wanted to build a developer tool that enables running python scripts downstream of dbt models. Since we launched the initial capabilities of fal, we have received an overwhelming amount of requests to run python scripts before, after and in between

After our Unbundling of Airflow [https://blog.fal.ai/the-unbundling-of-airflow-2/] article, it has been a wild few weeks here at Features & Labels. Gorkem’s [https://twitter.com/gorkemyurt] now-viral post resonated with tens of thousands of data people around the globe. Meanwhile, we have been hard at work cooking up

At Features & Labels [https://fal.ai], dbt [https://www.getdbt.com/] is one of our favorite tools to use while managing our data pipelines. It is a category defining tool (see Analytics Engineering [https://www.getdbt.com/what-is-analytics-engineering/]) that enables us to build data transformations without having to write boilerplate