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.
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.
Introducing Real-time Data to Machine Learning
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.
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.
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.
Popular Connectors in Our ML Community
Amazon MSK
Hopsworks FS
Confluent
Google Vertex AI
Feast
Build Streaming Pipelines for Real-Time Machine Learning
Get Inspired to Build with Bytewax
Online Machine Learning in Practice: Interactive dashboards to detect data anomalies in real time
Hear from the ML Community about Streaming with Bytewax
We have used Bytewax to develop a recommender system for a video streaming platform. I like to think in MapReduce terms when I do data processing, so I was super happy to find that Bytewax does precisely what I need and is easy to deploy and support.
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.