MACHINE LEARNING FOR TIME SERIES
With Shapelets Flow you can develop your own algorithms, integrate them with our time-series algorithms and publish them within Shapelets in a distributed manner in less than a minute
A SWISS ARMY KNIFE FOR DATA SCIENTISTS
Shapelets Flow is a distributed lambda architecture that captures custom algorithms and analytical flows and combines any Python library (TensorFlow, Keras, NumPy...) with Shapelets algorithms.
Extensibility
New algorithms can be added and used by any major language through the DSL frontend, regardless of its implementation language.
Integration
Each worker uses native libraries (NumPy, Pandas...) allowing full composition with existing solutions. Use Jupyter Notebooks, Flask, Stream lit or any of your preferred environment without constraints.
Simple Deployment
When new algorithms are ready, you can publish straight to our Crystal UI or your favourite BI tool. Users can quickly solve business problems reducing the cost of deployment to zero.
Empower your data science team and make the most of accelerated time-series data analytics
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