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Real-Time Feature Engineering

Ingest real-time data, perform data transformation, generate and share real-time features across environments and teams to build and deploy high-performance AI applications

Real-Time Feature Engineering
in Production

Feature engineering in machine learning is like an art—a very challenging art. Generating a new feature based on batch processing takes an enormous amount of work for ML teams, and those features must be used for the training stage as well as the inference layer. Feature engineering for real-time use cases is even more complex than for batch. Real-time pipelines require an extremely fast event-processing mechanism, that can running complex algorithms to calculate features in real time. With the growing business demand for real-time use cases such as fraud prediction, predictive maintenance and building real-time recommendation engines, ML teams are feeling immense pressure to solve the operational challenges of real-time feature engineering for machine learning in a simple and reproducible way. The Iguazio Data Science Platform solves these challenges, with one single logic to generate real-time and offline features for training and serving, and an extremely fast event processing mechanism to calculate features in real time.

Feature Engineering Made Simple

Feature Engineering Made Simple

Enable your data science team to create complex features using a simple API, and eliminate effort duplication and the consumption of excess engineering resources. Easily create aggregations on sliding windows, enrich your streaming events, calculate complex formulas and work on live-streaming events using an abstract API.

Native Feature Store Integration

Native Feature Store Integration

Consume any feature immediately, whether for production or feature analysis. All features, including real-time features, are managed, stored and analyzed in the Iguazio integrated feature store.

Ready for Production

Ready for Production

Eliminate the need to translate code and break the silos between data scientists and data engineers by automatically converting features written in Python into scalable low-latency production-ready functions.

Real-Time Graph

Real-Time Graph

Create a real-time graph with built-in libraries for common tasks, with just a few lines of code, to easily make sense of multi-step dependencies.

Benefits

Real-Time Automation

Real-Time Automation

Faster Deployment of AI

Faster Deployment of AI

Improved Model Accuracy

Improved Model Accuracy

Collaborate and Re-Use

Collaborate and Re-Use

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Data Science Platform Tutorials

Tutorial

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Data Science Platform Documentation

Documentation

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