MLflow
An open-source MLOps platform for managing the machine learning lifecycle — including experiment tracking, model versioning, deployment, and a model registry — across any ML framework.
How RevOps teams use it
RevOps data science teams use MLflow to track experiments for churn prediction, lead scoring, and deal win probability models — versioning features and hyperparameters so models can be reproduced, compared, and promoted to production.
Example use case
e.g., A RevOps data scientist uses MLflow to log 20 churn model experiments with different feature sets, compare AUC scores across runs, and deploy the best model to a Snowflake UDF for real-time customer health scoring.
Where it's used
Technical Disciplines
RevOps AI Engineer
Builds AI-powered tools and workflows that automate revenue tasks, surface insights, and reduce manual work across the sales, marketing, and CS stack.
ML Engineer
Builds machine learning models that power revenue predictions — from churn scoring and lead ranking to pipeline forecasting and pricing optimisation.