Machine Learning Operations
End-to-end machine learning workflow processes to manage AI/ML projects with improved delivery time, reduced defects, and make data science more productive
Simplify your machine learning lifecycle
The iterative ML lifecycle consists of many components such as data engineering, data preprocessing, feature engineering, model training, evaluation, deployment, monitoring, explainability, and much more. MLOps is focused on operationalising this building blocks of ML lifecycle for faster experimentation, evaluation, continuous integration and deployment of the machine learning lifecycle.
MLOps enables scalability and project life cycle management where hundreds of models can be scheduled, authored, managed, and monitored for continuous improvement. Machine learning models often need regulatory checks for the drifts in data, model performance and metrics, alerting at a specific threshold to enable greater transparency and faster response to such requests and ensures greater compliance.
