Predictive ML
Feature Engineering
Streamline feature creation and productionization across all types of data
Define features as code
Develop features using Tecton’s Python-based declarative framework. Data scientists and modelers can create features in their preferred notebook environment and transition from experimentation to production while maintaining consistency. Tecton ensures consistency and accelerates the path from idea to implementation.
Process batch, streaming, and real-time data with a unified approach
Say goodbye to separate data processing pipelines. Manage all feature pipelines across batch, streaming, and real-time data using a unified framework. Specialized modules simplify complex operations like time window aggregations and embedding generation, ensuring consistency throughout the ML pipeline.
Productionize features instantly
Deploy feature ideas into production-ready APIs and real-time data services with a single line of code. Features are immediately equipped with automated monitoring, SLAs, disaster recovery, and scalable infrastructure. Reduce time-to-production, minimize errors, and empower data scientists to iterate rapidly while maintaining enterprise-grade reliability and performance.
Build any transformation using Python and SQL
Write Python code, use pip packages, and craft SQL queries to meet specific needs. Tecton integrates with common data science tools and libraries. Leverage the Python ecosystem, access popular data science packages, and choose from processing engines like Tecton Rift, Spark, EMR, BigQuery, and Snowflake to create tailored features.
Eliminate skew with a single feature definition for training and serving
Maintain alignment between training data and production environments. Create historically accurate training datasets and ensure consistent feature computation across offline and online environments. Tecton enables:
- Training on point-in-time correct data
- Accelerated development without consistency concerns
- Improved model performance in production
- Prevention of difficult-to-identify train/serve skew issues
Predictive ML
