What sorts of side projects would be good for ML infrastructure?

I Googled for "machine learning side projects" and found this article which seems pretty good.

Making a delightful, easy-to-use front-end is much harder than people think, so I agree that it's probably not best to pick it up just for a side project (unless you're considering a pivot into that area). The back-end projects I've seen be successful are generally REST APIs. To expose it, you can bundle whatever service you make with a Postman collection (just include it in the GitHub and add instructions on using it to the README).

I may have been getting too caught up in doing something super specific to my area (MLops or infrastructure). I haven't used too much REST or any Postman, so I'll look into both of those. Thanks, Alex!

As an aside, another way to determine side projects in MLOps is by understanding what's out there. You can know this by reading what other companies are doing with infrastructure. For example,

• You can see how netflix schedules their jupyter notebook jobs using papermill. Here is the blog too.
• You can see how uber uses a feature store to manage features ML Engineers pass into machine learning models. Here is a link to open source versions of feature stores.
• The MLOps community has great resources too.

Not saying you should implement everything from scratch. But reading does help to see "What kind of work would I be associated with as an MLOps engineer".