Ask questions, share resources, and connect with others on all things MOps!
While Machine Learning Operations (MLOps) tends to be its own career path, it’s becoming more common for data scientists to need to know a little (to a lot!) more about how MLOps works. Whether you’re a team of one trying to get models into production, or a seasoned engineer with deep expertise, this is the place to connect, vent, and level up our MLOps skillset.
This is a new area for me so the more elementary the better! My team is pretty new and still working out what kind of workflow practices makes sense for us. Really nuts and bolts kinds of things. Would love to hear what how individuals and teams organize their workflows through the project lifecycle.
Guiding principles, best practices, checklists, templates? How do people actually do their work?
oooh I’d love to know what kind of content would be most helpful to you! it’s something we’ve been noodling on here - whether it’s blog posts or videos or whatever. MLOps can be a lot - we’ve got some stellar expertise on the team and would love to create the resources to help folks get started.
It’s kind of the wild, wild west out here. We’re just taking baby steps that we can hopefully adapt as we mature. Maybe folks have onboarding level guides or strategies for inculturating new team members. I’m trying to create templates and guides that make documentation, data storage, project organization, jira tickets, etc etc easy enough that we can actually do it.