AI governance encompasses the principles, policies, and practices that guide the responsible development and deployment of artificial intelligence systems.

Drift monitoring is a crucial process in machine learning that tracks changes in data distributions over time, ensuring model performance and reliability in production environments.

An Enterprise Data Science Platform is a comprehensive suite of tools and infrastructure designed to support the end-to-end data science workflow in large organizations.

A feature is an individual measurable property or characteristic of a phenomenon being observed. Features are the input variables used in predictive modeling and statistical analysis.

An integrated development environment (IDE) combines essential tools for software development into a single application, enhancing productivity for data scientists and engineers.

The MLOps lifecycle encompasses the end-to-end process of developing, deploying, and maintaining machine learning models in production, integrating software engineering best practices with data science workflows.

An MLOps platform integrates tools and processes to streamline the machine learning lifecycle, from development to deployment and monitoring.

Model development in data science involves creating, training, and refining predictive models to extract insights from data and solve complex problems.

A model registry is a centralized repository for managing and versioning machine learning models throughout their lifecycle.

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.