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Harnessing CrateDB’s Multi-Model Capabilities for AI-Powered Applications

In this video, we explore the integration of CrateDB and PyCaret to detect anomalies in machine data, crucial for identifying potential failures or inefficiencies in technological systems. CrateDB's capability for handling large-scale data with ease pairs seamlessly with PyCaret's low-code approach to machine learning, offering a streamlined path to uncovering insights within vast datasets.

Through this tutorial, we'll demonstrate how to harness CrateDB for efficient data retrieval and leverage PyCaret for its powerful anomaly detection algorithms. This concise guide is designed to equip you with the knowledge to perform anomaly analysis effectively, ensuring the reliability and security of your operations.

Jupyter Notebook