SymetryML is driving an evolution in machine learning with its cutting edge, Kafka-native, streaming machine learning, which provides several unique benefits that conventional (batch) learning can’t support.
SymetryML's proprietary online learning algorithms & models provide streaming machine learning & exploration, unlocking an entirely new level of real-time capabilities.
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SymetryML integrates very well with Kafka allowing for the unique capabilities:
- Separation between predictive model building and data ingestion. This allows to perform continuous ML - as the stream of data is coming from a Kafka topic – to build various predictive models using any of your features as the target variables as well as using any other features as inputs variables.
- No Rescan of your Kafka Topic is ever needed to build multiple, different predictive models.
- SymetryML can scale with many Kafka Topics with multiple partitions, this will be covered in a subsequent blog post.
- SymetryML can automatically adapt to Kafka Topic schema change. If your data schema changes a lot, this will is great feature to have.
- Extensive exploration API that allows you to get various real-time metrics from your streaming data.
Other Technical Highlights:
- Explore your data in real time with the Exploration API
- Real-Time support for many for supervised classification, regression and sequence models
- Various Real-time unsupervised anomaly & clusterning algorithms & models
- Rest-API for developers & Web-UI for business & analytics users
Streaming ML with Kafka
SymetryML "Fusion Project"