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An anomaly detection framework anyone can use
- Sarah Alnegheimish, a PhD student at MIT, is focused on developing Orion, an open-source machine learning framework for anomaly detection.
- Coming from a background where education was highly valued, Alnegheimish believes in making machine learning tools accessible to all.
- Alnegheimish's master thesis on time series anomaly detection led her to create Orion, which uses statistical and machine learning models.
- Orion offers transparency and accessibility through open-source code, allowing users to investigate anomalies without deep machine learning expertise.
- Alnegheimish's current research involves repurposing pre-trained models for anomaly detection tasks, aiming to save time and computational costs.
- She emphasizes on making her work accessible by developing systems that simplify the use of machine learning models for others.
- Her system development approach involves finding the right abstractions that provide universal representation for all models.
- Alnegheimish has mentored students to develop models using the abstractions she employs, showcasing the effectiveness of her system design.
- She has also implemented a large language model (LLM) agent to facilitate user interaction with Orion through simple commands.
- With over 120,000 downloads and positive user feedback on Github, Orion is making AI more accessible and seeing real-time adoption through open source.
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