MLOps is a set of practices and processes that aim to improve the quality, reliability, and timeliness of machine learning (ML) models within an organization. The main goal of MLOps is to enable organizations rapidly and efficiently develop, deploy, and maintain ML models at scale, which can lead to improved efficiency, better decision-making, and greater innovation.
MLOps involves the following key practices:
- Data preparation : This involves preparing data for use in ML models, including cleaning, pre-processing, and transforming the data.
- Model development : This involves developing and testing ML models, using techniques such as cross-validation and hyperparameter tuning.
- Model deployment : This involves deploying ML models to production environments, such as cloud platforms or on-premises infrastructure.
- Model monitoring : This involves monitoring ML models in production environments to ensure that they are performing as expected.
- Model retraining : This involves periodically retraining ML models to keep them up to date with changes in data or business requirements.