This repo contains what I've learned so far about how to design and develop a Machine Learning Pipeline (also called MLOps). I hope this repo will help others who show the same interest with me in this filed. I will try my best to keep this repo updated frequently, so stay tune.
I will put some references that I think may be useful below if you guys want to learn more.
MLOps is the short term of Machine Learning Operations. It represents a set of practices that aim to simplify workflow process and automate machine learning/deep learning deployments.
Ops in MLOps comes from DevOps, a short term of Developments and Operations. To operationalize something means to bring it into production, which includes deploying, monitoring, and maitaining it. MLOps is a set of tools and best practices for bringing ML into production.
