Blog post "LLMs for relational data"

[alxn4]

https://www.linkedin.com/feed/update/urn:li:activity:7254145491779690497/

https://www.notion.so/code17-io/21-10-LLMs-for-relational-data-11f38d2c567e803a9e68dcab54f7f7ef

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LLMs work well for unstructured data like text and images. But when it comes to predictive AI on relational and time-series data, LLMs hit a wall. The issue? Generative models only repeat what they have learned from training, and that will not help uncover the prediction-relevant patterns unique to your application—whether it is customer behavior, financial transactions, or supply chain activities.

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.

🚫 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.

💡 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 (https://lnkd.in/g8Ttvg2Z).