All the ML without all the ops
Our powerful data governance and identity resolution features provide AI and ML teams model-ready data
40,000+ sites and apps run RudderStack
Build powerful AI & ML models on clean customer data
From 90% cleaning, 10% modeling...
Data scientists spend more time prepping data than they do building models, forced to make tradeoffs between data engineering and machine learning.
...to automated ML insights
With RudderStack, data teams can rapidly generate churn and lead score models on clean data, and enable their data science teams to accelerate more complex projects.
Accelerating AI/ML with high-quality data
Learn how to use automate data collection and unification to build
a strong foundation for any kind of AI/ML use case.
Automatically generate identity graphs for any entity, then compute ML-ready features in your warehouse.
Validate and fix new or bad data in-flight before it is loaded into your data lake or warehouse for modeling and training.
Instantly tap into RudderStack's library of customizable ML models and append predictive attributes to customer profiles.
Thoren Palacio, Data Leader at TommyJohn
Build high-quality models on a foundation of clean, standardized event and user data.
Manipulate raw data into features using a low code configuration vs. full engineering effort.
Improve model accuracy with comprehensive, configurable, automated identity graphs.
Train and tune our curated churn and lead score models in your warehouse without any ML ops.
Schedule model retraining based on new data and get alerts on degraded model performance.
Automatically add predictive features to customer profiles and sync them to business tools.
Go from data to predictions faster
Optimize for time spent on improving models and delivering results to stakeholders, not wrangling data. With RudderStack you can quickly build and deliver predictions across your data stack to drive meaningful outcomes.
Deliver AI/ML features faster
Easily compute complex features and develop predictive traits, then deliver them directly to stakeholders.
Always start with model-ready data
Let RudderStack's standardized schemas and data governance do the dirty work so you can focus on model development.
Tune models with your existing workflow
Run tight iteration loops with built-in training and monitoring. No ML Ops means you can drive business value sooner.