Machine learning operations (MLOps) is becoming an exciting space as we figure out the best practices and technologies to deploy machine learning models in the real world. MLOps enable ML teams to build responsible and scalable machine learning systems and infrastructure. This facilitates tasks that range from risk assessment to building and testing to monitoring. While still in its infancy, MLOps has attracted machine learning engineers and software engineers in general. With every new paradigm comes new challenges and opportunities to learn. If you are curious about MLOPs and why it matters in designing ML systems, I’ve put together a collection of my favorite references.
Check it out: https://github.com/dair-ai/MLOPs-Primer
