MLOps (Machine Learning Operations)

MLOps (Machine Learning Operations) is a software development process that is used to manage the deployment, monitoring, and maintenance of machine learning (ML) models. It brings together the processes involved in ML development and operations such as data engineering, model training and testing, model deployment, and model performance monitoring.

MLOps is a relatively new discipline that combines the skills of software engineering, machine learning engineering, and DevOps. It provides a unified platform for ML teams to collaborate better and effectively manage the lifecycle of ML models. This includes creating reproducible ML pipelines, automating the orchestration of data, model training, and model deployment. It also helps streamlining model management to enable developers to quickly identify and react to model accuracy drift with automated model restarts and retraining.

MLOps provides a platform where data scientists, software engineers, and DevOps engineers can collaborate to build, deploy, and monitor ML-driven applications. Developers can then use MLOps to optimize data pipelines, automate the retraining of models, and quickly deploy new versions of models into production. This helps developers to reduce time to market for new ML applications and makes it easier to deploy and maintain models while controlling costs.

MLOps also goes hand-in-hand with the Continuous Integration and Continuous Delivery (CI/CD) practices used to automate software development workflows and improve the overall quality of the software system being created. This ensures that the iterations of a given model can be delivered faster with greater accuracy, while making sure it remains production-ready.

In short, MLOps is a platform that combines the elements of traditional software engineering with machine learning in order to deploy and manage models in a cost-effective and efficient fashion. It helps data science and developers teams to quickly iterate on models, automate the deployment and maintenance of models, and deliver models to production with greater accuracy.