Machine Learning

Build ML-Powered Applications with Streaming Data

The best ML applications leverage continuously generated data in real time. The user friendly Bytewax Python API enables your team to leverage the ML ecosystem with streaming data to deliver powerful user experiences.

Use Cases from Our Community

Introducing Real-time Data to Machine Learning

Manufacturing

Predictive Maintenance

Bytewax facilitates the real-time data processing from manufacturing sensors through machine learning models to predict equipment failures, enabling effective predictive maintenance and reducing unplanned downtime.

Finance

Fraud Detection

Machine learning algorithms analyze transactional data streams in real time to detect fraud, with Bytewax handling the necessary high-velocity data. This supports financial institutions in maintaining security and customer trust.

E-Commerce

Personalized Recommendations

ML models use real-time clickstreams to dynamically personalize product recommendations. Bytewax enables efficient data processing, allowing real-time recommendations based on current customer behaviors and preferences.

ML Connectors

Popular Connectors in Our ML Community

Open source

Confluent

Sink & Source
Premium

Hopsworks FS

Sink
Premium

Google Vertex AI

Source
Open source

Apache Kafka

Source & Sink
Premium

Amazon SageMaker

Sink
Premium

Feast

Sink
Architecture

Build Streaming Pipelines for Real-Time Machine Learning

ML Architecture

Machine Learning models are only as good as the input features you feed at training and inference time. For many real-world applications these features must be generated and served as fast as possible, so the ML system produces the best predictions possible.

COMMUNITY VOICES

Hear from the ML Community about Streaming with Bytewax

The key difference between Apache Spark and Bytewax for me teaching my class on ML systems is that it takes me around six lectures to bring students up to the level where they can begin utilizing Spark. However, I only need one lecture to do the same with Bytewax.

I was a fan of batch things but after I discovered how easy is to implement a streaming pipelines with Bytewax, I changed my mind 😅

Libraries like Bytewax 🐝 expose a pure Python API on top of a highly-efficient language like Rust. So you get the best of both worlds. Rust's speed and performance, plus Python' rich ecosystem of libraries.