Awesome GitHub RepositoriesIn-Database Machine Learning

Azure Data Studio is a cross-platform SQL database management IDE used for writing queries, managing schemas, and administering relational databases. It functions as a comprehensive environment for relational database management, providing a structured interface for executing SQL queries and browsing database objects. The platform is distinguished by its interactive data notebooks, which combine executable code cells, narrative text, and visualizations for data analysis. It also includes specialized tools for database migration, allowing users to assess and transfer schemas and data from on-p

PostgresML is a machine learning database extension for PostgreSQL that integrates model training and inference directly into the database. It functions as an in-database AI platform and vector database, enabling the execution of large language models and natural language processing tasks on stored records without exporting data to external services. The system distinguishes itself by utilizing GPU acceleration to minimize latency during model predictions and employing a hybrid storage engine that maintains relational data alongside high-dimensional vectors. It allows for the building and fin

GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without

Superduper is an AI agent development kit and LLM application framework designed to build autonomous agents and data-driven applications. It functions as a RAG orchestration platform and vector search infrastructure, coordinating AI models with database storage to perform multi-step computations and actions using persisted data states. The project distinguishes itself by providing a database-integrated machine learning pipeline that executes training and inference tasks directly on data hosted within SQL and NoSQL databases. It allows for the deployment of self-hosted AI infrastructure on pri

sqlflow is a SQL machine learning engine and orchestrator designed for training, deploying, and explaining machine learning models using extended SQL query syntax. It enables in-database machine learning by connecting database engines to external machine learning toolkits, allowing users to define training datasets and hyperparameters directly through queries. The system functions as a prediction interface and explainability tool. It allows for generating classifications and predictions on database records by calling model functions within standard SQL statements and provides a workflow to in

Pigsty is a comprehensive database infrastructure orchestration platform designed to automate the full lifecycle of high-availability PostgreSQL clusters. It functions as an infrastructure-as-code framework that manages cluster coordination, node provisioning, and service discovery through idempotent playbooks. By integrating distributed consensus mechanisms, the platform ensures automated failover and consistent state enforcement across diverse environments, including bare metal and virtualized infrastructure. The platform distinguishes itself through a robust suite of operational capabiliti