MLOps

Streamline your ML workflow management

Have you ever ever copy-pasted chunks of utility code between projects, leading to multiple versions of the identical code living in several repositories? Or, perhaps, you needed to make pull requests to tens of projects after the name of the GCP bucket wherein you store your data was updated?

Situations described above arise way too often in ML teams, and their consequences vary from a single developer’s annoyance to the team’s inability to ship their code as needed. Luckily, there’s a treatment.

Let’s dive into the world of monorepos, an architecture widely adopted in major tech firms like Google, and the way they will enhance your ML workflows. A monorepo offers a plethora of benefits which, despite some drawbacks, make it a compelling alternative for managing complex machine learning ecosystems.

We’ll briefly debate monorepos’ merits and demerits, examine why it’s a superb architecture alternative for machine learning teams, and peek into how Big Tech is using it. Finally, we’ll see find out how to harness the ability of the Pants construct system to prepare your machine learning monorepo into a strong CI/CD construct system.

Strap in as we embark on this journey to streamline your ML project management.

This text was first published on the neptune.ai blog.

A monorepo (short for monolithic repository) is a software development strategy where code for a lot of projects is stored in the identical repository. The concept will be as broad as all of the corporate code written in quite a lot of programming languages stored together (did any person say Google?) or as narrow as a few Python projects developed by a small team thrown right into a single repository.

On this blog post, we concentrate on repositories storing machine learning code.