#feature engineering

NVTabular is a library for feature engineering and preprocessing of large-scale tabular datasets, optimized for recommender systems. It efficiently handles terabyte-scale data with GPU acceleration via RAPIDS Dask-cuDF. As part of NVIDIA's Merlin framework, it integrates with other tools to streamline model training and deployment. NVTabular solves issues like large datasets and complex preprocessing, facilitating quick experimentation and deployment. It operates beyond standard memory limitations, allowing for smooth data processing and resource optimization.

Lightwood simplifies the machine learning process by allowing customization with JSON syntax. It supports various data types for complex tasks like time-series analysis, enabling users to tailor machine learning pipelines without writing repetitive code.

NVIDIA Merlin is an open-source library designed to enhance the performance of recommender systems using GPU acceleration. This library supports building scalable solutions by overcoming obstacles in feature engineering, training, and inference. Components like NVTabular, HugeCTR, Merlin Models, and Transformers4Rec facilitate data preprocessing, training, and recommendation tasks. Integration with Triton Inference Server enables efficient production deployment.

Chronon enhances AI/ML data management by simplifying feature definitions from raw data and enabling efficient batch and stream processing. It supports low-latency serving and maintains consistency and observability, allowing organizations to utilize extensive data sets efficiently. The platform features integrated orchestration, scalable backfills, and innovative APIs to ensure the accuracy of both real-time and historical data. Designed for models requiring real-time data handling, Chronon addresses infrastructure challenges, focusing on model development over data operations.