Accurate, well-structured data is a necessity for machine learning models. Get everyone on the same page with Causal’s shared data model. Define what data needs to flow in and out of your model, and keep it updated easily through your own revision control system.
Causal’s data model provides built-in data governance by preventing accidental changes to the front-end that damage inputs to ML models. Custom, typesafe SDKs ensure that no one on the team can break your pipelines.
Causal's plug-in architecture makes it simple to incorporate any data source as an input to your models. Use data from any source you need, without having to pipe it through to your front end.
Causal gathers all the data needed for your model, passes it into the model, and passes the model responses back into your product. As that happens, it records all the data you need and delivers it to your data warehouse in exactly the format needed for training. You get accurate data with zero production-training skew and no chance your model can “peek” into the future.
Causal's web tools make it easy for anyone to swap in new models, without needing to write or deploy new code. Anyone on the team can easily roll out a model, load test a new model by gradually increasing its traffic, or A/B test new models.
Once you've set up a model input for one model, it can easily be reused in any other model. No additional data engineering effort is required.
Causal supports common ML performance metrics like NDCG and MRR out of the box. It also allows you to monitor your models using business metrics, like revenue per user or user retention.
Causal's QA tools make it easy to view experimental versions of your app experience and make sure your tracking code is set up correctly before you roll it out. With Causal's Event Viewer, you can see how data will flow to your models.
We had four A/B testing platforms—none of which met our needs—and I’ve been trying for years to unify them and solve our problems across our stack. We’ve started eliminating those other platforms and Causal has increased the velocity of our experimentation.”
Co-Founder & Chief Data Officer, Jobcase
With Causal, I don’t have to worry about the data being off. So I can be more productive around new things to implement and not have doubts around pushing out experiments.”
Staff Software Engineer, Summari
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