Integrations

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

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

LangChain is a development framework that makes it easy to construct LLM-powered applications using a variety of different providers for different services. ApertureDB's capabilities fit within the LangChain ecosystem in two main areas: vector store and graph database.

🗃️ Query Languages

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