Introduction
or Machine Learning Operations are a set of processes and practices to automate the production lifecycle for machine learning models. It is an approach used to bridge the gap between the development environment and production environment while ensuring that automated tests, security checks, and other auditing measures are kept in place. MLOps supports scalability, reliability, and governance by delivering models that are ready for production.
What is MLOps?
is designed to continuously build, test, integrate, deploy, monitor and manage machine learning models. It enables data science teams to quickly move from idea generation to model deployment without sacrificing accuracy or reliability. MLOps automates the entire end-to-end process of machine learning delivery by optimizing the model life cycle with a standardized automation framework.
How Does MLOps Work?
MLOps process consists of 4 main stages: development, integration, deployment and monitoring. In the development stage, the data scientist will build their machine learning models using software tools such as Tensor Flow or PyTorch. During integration, various components such as data pipelines, model training code and feature engineering are integrated into a single system. The deployment stage involves deploying the trained models to production environments where they can be used by end-users. Finally, during monitoring new data points are tracked and monitored continuously in order to make sure that the model remains accurate over time.
MLOps Automation Processes
order for MLOps automation processes to be successful there needs to be some existing infrastructure in place. This includes Continuous integration/Continuous Delivery (CI/CD) pipelines which facilitate faster delivery of software updates through automated testing; Infrastructure as Code (Iac), which allows for easy setup of cloud services through code; Version Control Systems (VCS) which allow collaboration on projects across multiple developers; and platforms such as Kubernetes which allow deployment of applications at scale.
