An anomaly detection framework anyone can use

In the rapidly evolving landscape of artificial intelligence, a groundbreaking initiative is making machine learning systems more accessible, transparent, and trustworthy for everyone. Sarah Alnegheimish, a pioneering PhD student in Principal Research Scientist Kalyan Veeramachaneni’s Data-to-AI group at MIT’s Laboratory for Information and Decision Systems (LIDS), is at the forefront of this movement. Her dedication has culminated in the development of Orion, an open-source, user-friendly machine learning framework and time series library designed to democratize anomaly detection across large-scale industrial and operational environments.

Alnegheimish’s vision is deeply rooted in her upbringing. Influenced by her parents, both educators, she understood early that knowledge should be shared freely. This ethos was further solidified by her own experience with MIT OpenCourseWare while pursuing her bachelor’s degree at King Saud University (KSU). “I learned to view accessibility as the key to adoption. To strive for impact, new technology needs to be accessed and assessed by those who need it,” Alnegheimish states, emphasizing the core purpose behind open-source development.

Her journey to creating Orion began during her master’s thesis, where she focused on time series anomaly detection – the critical process of identifying unusual patterns in data that can signal anything from cybersecurity threats to potential machinery failures or health complications. This foundational research led to the initial design of Orion, a framework engineered to simplify complex analytical tasks.

Orion leverages sophisticated statistical and machine learning models, continuously logging and maintaining them for optimal performance. A key innovation is that users do not need to be machine learning experts to operate the system. It enables effortless analysis of signals, comparison of anomaly detection methods, and in-depth investigation of anomalies through an intuitive, end-to-end program. Crucially, the framework, its code, and associated datasets are all open-sourced, embodying Alnegheimish’s commitment to transparency.

“With open source, accessibility and transparency are directly achieved. You have unrestricted access to the code, where you can investigate how the model works,” Alnegheimish explains. Orion takes transparency a step further by labeling every step in the model, fostering user trust even before they witness its reliability. This approach transforms advanced AI tools from academic curiosities into practical, deployable solutions for a broad audience. “We’re trying to take all these machine learning algorithms and put them in one place so anyone can use our models off-the-shelf,” she adds.

In her ongoing PhD research, Alnegheimish is pushing the boundaries of anomaly detection by exploring innovative uses for pre-trained models. Traditionally, machine learning models required training from scratch, a resource-intensive process. Her work focuses on repurposing these pre-trained models, which already capture intricate time-series data patterns, for anomaly detection through prompt-engineering, without additional training. While current results show promise, she believes this method will eventually rival the success rates of independently trained models.

The simultaneous development of models and systems has been a cornerstone of Alnegheimish’s accessible design philosophy. She prioritized finding universal abstractions that simplify complex components, allowing any model to seamlessly integrate by standardizing input and output. This foresight has ensured stability and reliability for over six years, a testament to its robust design. Her mentorship experiences further validate this approach; students readily grasped the system, developing their own models using the established abstractions.

To further enhance accessibility, Alnegheimish has implemented an LLM agent that acts as a mediator, allowing users to interact with Orion using just two simple commands: ‘Fit’ for training and ‘Detect’ for anomaly identification. This mirrors the user-friendly experience of platforms like ChatGPT, abstracting away internal complexities. “The ultimate goal of what I’ve tried to do is make AI more accessible to everyone,” she asserts.

Orion’s impact is already significant, boasting over 120,000 downloads and more than a thousand users who have starred its repository on GitHub. This tangible adoption demonstrates a shift in how research impact is measured, moving beyond traditional citations to real-time, widespread use in the open-source community. Sarah Alnegheimish’s work with Orion is not just advancing anomaly detection; it’s building a future where sophisticated AI is truly for everyone.