  Machine Learning Operations (MLOps) is becoming a highly crucial part of
businesses looking to capitalize on the benefits of AI and ML models. This
research presents a detailed review of MLOps, its benefits, difficulties,
evolutions, and important underlying technologies such as MLOps frameworks,
Docker, GitHub actions, and Kubernetes. The MLOps workflow, which includes
model design, deployment, and operations, is explained in detail along with the
various tools necessary for both model and data exploration and deployment.
This article also puts light on the end-to-end production of ML projects using
various maturity levels of automated pipelines, with the least at no automation
at all and the highest with complete CI/CD and CT capabilities. Furthermore, a
detailed example of an enterprise-level MLOps project for an object detection
service is used to explain the workflow of the technology in a real-world
scenario. For this purpose, a web application hosting a pre-trained model from
TensorFlow 2 Model Zoo is packaged and deployed to the internet making sure
that the system is scalable, reliable, and optimized for deployment at an
enterprise level.
