# Leveraging the Plain Old Python Function

DevFeed: [Leveraging the Plain Old Python Function](<https://devfeed.tech/articles/leveraging-the-plain-old-python-function-29340.md>)

Original publisher: [Read original article](<https://multithreaded.stitchfix.com/blog/2022/12/14/plain-old-python-functions/>)

Published: 2022-12-14T09:00:00Z

Content type: article

Language: en

Sources: [Stitch Fix](<https://devfeed.tech/sources/stitch-fix.md>)

Topics: [Python](<https://devfeed.tech/topics/python.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [Framework](<https://devfeed.tech/topics/framework.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [apis](<https://devfeed.tech/tags/apis.md>), [blog-post](<https://devfeed.tech/tags/blog-post.md>), [dsl](<https://devfeed.tech/tags/dsl.md>), [functional](<https://devfeed.tech/tags/functional.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [model-deployment](<https://devfeed.tech/tags/model-deployment.md>), [python](<https://devfeed.tech/tags/python.md>)

## AI overview

Stitch Fix describes a function-first approach to data science APIs and MLOps. Instead of building custom model-deployment mechanisms, microservices, and tightly coupled data transformations, data scientists can use plain Python functions backed by platform infrastructure.

## Source excerpt

The role of the full-stack-data-scientist is not what it once was. With the advent of more powerful tooling, new industry standards in MLOps, and greater investment in platforms, the day-to-day of a data scientist has changed significantly at Stitch Fix. The difference, however, is subtle. The structure of their job remains the same - engineers still do not write ETLs and data scientists function as generalists, but they now have to think on a higher level. Their job is constantly getting more and more complex--the business needs are in flux and the infrastructure they use is more powerful than it ever was. The old strategy of cobbling together complex systems will only end in stressed-out data scientists with too much infrastructure on their plate. To avoid this cycle of complexity, Stitch Fix invests in a platform team to innovate new ways of supporting a data scientist's engineering needs. Rather than constructing custom model-deployment mechanisms, building microservices from the ground up, and managing highly interdependent chains of data transformations, data scientists at Stitch Fix can leverage powerful infrastructure by constructing plain old Python functions to represent their needs. In this blog post we're going to take a different approach than usual. Rather than digging into a specific piece of technology, we'll present our philosophy of functions for data science APIs and back it up with some motivating examples. We'll explain the power of functions as a DSL, share some successes we've had using functional interfaces to build our MLOps stack, and connect our approach with external, open-source frameworks that the industry is beginning to adopt. Our goal is to convince you that a function-first approach will enable data practitioners to do more while doing less. The functional approach allows them to plug into the business in a scalable manner while avoiding the complexity of managing infrastructure and architectural decisions. On Functions and Functiona