Predictive ML

Training Data Generation

Automate point-in-time correct training data creation and backfilling for robust and reliable ML models

Eliminate data leakage with point-in-time correct feature values

Tecton’s automated feature time management removes the need for manual timestamp-based logic coding, saving time and reducing errors. Prevent data leakage and eliminate a common source of training-serving skew, ensuring historically accurate training data. Build ML models with confidence using training data that accurately represents real-world conditions.

Scale effortlessly to massive data sets

Tecton’s training data generation accommodates thousands of features and billions of rows in a single training dataset, supporting 1000+ columns across multiple dimensions. Add all relevant signals to a model without performance concerns. Seamlessly bring together multiple types of features into a single dataset, avoiding complex data engineering to join disparate sources.

Reuse features across multiple use cases

Avoid redundant compute costs by reusing features across different use cases. Easily discover and repurpose existing features, accelerating model development and optimizing resources. Seamlessly combine batch, streaming, and real-time features to create comprehensive training datasets. Save every dataset for future reference, reuse, or compliance, maximizing the value of your data preparation efforts.

Generate training data quickly with a single command

Generate correct training data with a single command, reducing time and effort required for dataset preparation. Create training data locally in your notebook or on your laptop for blazing-fast iteration speed. For larger resource needs, generate training data on large-scale remote clusters with a single line of code. Enable data science teams to focus on model development and iteration rather than data preparation.