How it feels to learn MLOps in 2021

Adapted from [1]

Disclaimer: No MLOps tool was harmed during the creation of this article. This post is inspired by a classic article “It’s the future” from CircleCI and a nifty post about Javascript frameworks. This is just a Sunday opinion piece, and like any MLOps platform, it should be taken with a pinch of salt.

Hey, I got this Loan Default Prediction model, and my boss said you could help me deploy it? I haven’t coded much production ML software lately, and I’ve heard MLOps is the way to go. You are the most up-to date Ops person around here, right?

• The correct title is MLOps engineer, but yeah, I can help with that. MLOps in 2021 is my thing. Canine posture detection, Kitchen Edge ML, NLP for flying IoT, Active learning for Self-driving TV remotes, anything to help. I spent the past month at ODSC, MLOps Summit, PyData, and KDD, so I probably can describe the latest technologies to build and deploy ML products.

Nice. My model takes in a set of numerical features and returns a probability of Loan Default, so I just need a simple REST endpoint to respond to requests from the front-end application. I was thinking of a simple Flask app to do the prediction and return the probabilities, what do you think?

• Hmm yeah that would be too backward, no one uses Flask for predictive endpoints anymore. You should try KServe, it is…