LLMs for relational data?
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It's a classic, around since the 70s, used it myself back in uni, but data science needs have clearly evolved.
With TSFresh, Blue Yonder's engineering team set the sails for brute-force feature engineering, combining a large set of 100+ predefined feature templates with traditional feature selection methods. This approach performs well, but their implementations limitations: computational complexity on real-world data and limited support for multivariate time series.
Manual feature engineering? It is neither fun nor scalable. That is why we built getML! By generalizing gradient boosting to multi-relational decision trees, getML brings supervised learning directly to raw relational data.
đźš« Generative AI misses the complexity of relational data. getML solves this by creating prediction models from your actual relational data, uncovering patterns generative AI overlooks.
🚀 getML Feature Learning evaluates billions of features across multiple tables and joins automatically, finding insights that would take weeks to discover manually—leveraging gradient boosting for relational data.
đź’ˇ Less Code, More Focus. With getML, replace 10,000 lines of feature engineering code with under 100, so you can focus on optimizing models, not debugging SQL.
⏱ Results in Days, Not Months. With getML, predictive modeling on relational data moves from months to days—speeding up development without sacrificing performance.
🔄 Stay Ahead of Feature Drift. As your data evolves, so do the patterns. Just call pipeline.fit to retrain your getML models and ensure your predictions stay accurate.
Curious about getML Feature Learning? Lets chat for a deep dive, or explore our notebook: Predicting Robot Arm Force with Sensor Data, created in collaboration with SIEMENS.