📄️ Labeling
Labeling is a common task in supervised machine learning involving multiple
📄️ Training and Inference
A key purpose of collecting labeled data is to use it for training and validation of ML models
📄️ LangChain
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A typical machine Learning (ML) workflow involves a variety of tasks, including data collection, labeling, curation, training, validation, and inference. These tasks generate or consume a significant amount of data that needs to be stored and managed efficiently. ApertureDB is built to be the unifying backend for the various steps in a ML/AI pipeline. It already integrates with some popular libraries and frameworks.
Labeling is a common task in supervised machine learning involving multiple
A key purpose of collecting labeled data is to use it for training and validation of ML models
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