This article goes through the similarities and differences between DevOps and MLOps as well as platforms that help enable MLOps

As the field of machine learning has matured in recent years, the need for integrating automatic continuous integration (CI), continuous delivery (CD) and continuous training (CT) to machine learning systems has increased. The application of DevOps philosophy to a machine learning system has been termed MLOps. The aim of MLOps is to fuse together the machine learning system development (ML) and machine learning system operation (Ops) together.

What is DevOps?

DevOps is a practice used by individuals and teams when developing software systems. The benefits individuals and teams can obtain through a DevOps culture and practice includes:

1. Rapid development life cycles
2. Deployment velocity
3. Code quality through the use of testing.

#cloud-computing #machine-learning #mlops #devops