New internal machine learning framework
Over the past few weeks we have been reporting on LLMs. Today we have time series again. Many listeners will be pleased. Our guest is Dr Julian Feinauer. He explains how he uses Apache IoTDB and Timecho to implement a model in the database. In the news section, Peter Seeberg reports on an interesting paper: Can an LLM control a HVAC system? Yes, say the authors. Read more in the podcast. Back to the AINode.
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This release introduces "AINode", the internal machine learning framework, and brings a major upgrade to the permissions module, supporting sequence-level permissions. In addition, various optimisations have been made to features such as views and stream processing, improving the usability of the product while increasing release stability and overall performance.
AINode" internal machine learning framework
What is AINode? AINode is the third internal node in Apache IoTDB, after ConfigNode and DataNode. This node extends IoTDB's ability to perform machine learning analysis on time series data by interacting with the DataNode and ConfigNode clusters. AINode enables the integration of existing machine learning models by registering them and performing time series analysis tasks using simple SQL statements on specified time series data. This seamlessly combines model creation, management and inference within the database engine. The framework currently supports popular machine learning algorithms or proprietary models for typical time series analysis scenarios such as time series prediction and anomaly detection.
New internal machine learning framework
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