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Automated
Operational
Simple
Put data first where it belongs
Embrace a declarative AI workflow
Be ready for day 2 operations
Integrate directly with Snowflake, Redshift, BigQuery, and Databricks
Leverage all your production data without costly integration efforts
Get predictions immediately in your existing BI and operational tools
Leverage SQL and your data warehouse for feature engineering
Prevent data leakage with support for feature time travel
Join relational features automatically into new models
Build and deploy models 10x faster with a simple declarative workflow
Get state-of-the-art performance with a relational AutoML engine
Free up machine learning engineers for R&D and other projects
Automate model retraining, promotion, and rescoring
Eliminate integration pipelines by building on your data warehouse
Gain full visibility into the performance of models over time
Collaborate with your team in a shared workspace with fine-grained access control.
Track changes and audit the full history of feature, model and prediction model updates.
Avoid feature leakage with point-in-time correct features and automatic temporal joins.
Keep predictions up-to-date efficiently using either incremental or full refresh policies.
Monitor performance over time to avoid data drift and costly prediction errors.
Build models from the UI or drive your production models directly from a CLI/SDK or dbt.
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