Embeddings Generation

Productive ML

Embeddings Generation

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.

How to Define Features

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.

How Feature Views Work

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.

Feature Development Workflow

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

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.

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

Creating and Managing Features