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At the Vector Institute, we are dedicated to advancing artificial intelligence (AI) research and translating cutting-edge innovations into real-world solutions. Our open-source projects reflect our commitment to collaboration, transparency, and the responsible deployment of AI technologies. Here, you’ll find tools, frameworks, and resources that empower organizations and researchers to harness the full potential of AI.
Our open-source contributions aim to bridge the gap between theoretical research and practical application of AI. We build tools, MVPs, reference implementations and educational resources that empower researchers, developers, and organizations to innovate and solve real-world problems with AI. Our projects span various domains and are designed to be accessible, adaptable, and impactful.
Here are a few key projects that exemplify our mission:
Explore the Vector Implementation Catalog — a showcase of several AI implementations across topics and domains. Each implementation includes links to GitHub repositories, associated datasets, and research papers, making it easy to reproduce and build upon our work.
We believe that open-source AI tools are essential for fostering innovation and addressing global challenges. By sharing our work, we aim to:
We welcome contributions from the global AI community. Whether you’re a researcher, developer, or industry professional, there are many ways to engage with our projects:
Stay updated on our latest projects and announcements:
Together, we can unlock the transformative potential of AI and drive meaningful impact across industries and society.
Reference Implementations for Multi-modal Synthetic Data Generation Bootcamp
An interpretable foundation model of the patient clinical timeline: event forecasting, calibrated time-to-event alerts, and concept-level interpretability, benchmarked head-to-head against tuned GBMs, tabular foundation models, and survival baselines on MIMIC-IV, eICU, and GEMINI via the MEDS standard.
AI Engineering template repository using `uv` as python dependency/package manager.
AI Engineering template repository for implementations
A simple static website using Next.js that adheres to Vector branding guidelines
MLflow integration for Inspect AI evals: log runs, metrics, artifacts, and traces.
A template for deploying demos using GCP Cloud Run & vLLM
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