Sintel: A Machine Learning Framework to Extract Insights from Signals

The detection of anomalies in time series data is a critical task with many monitoring applications. Existing systems often fail to encompass an end-to-end detection process, to facilitate comparative analysis of various anomaly detection methods, or to incorporate human knowledge to refine output. This precludes current methods from being used in real-world settings by practitioners who are not ML experts. In this paper, we introduce Sintel, a machine learning framework for end-to-end time series tasks such as anomaly detection. The framework uses state-of-the-art approaches to support all steps of the anomaly detection process. Sintel logs the entire anomaly detection journey, providing detailed documentation of anomalies over time. It enables users to analyze signals, compare methods, and investigate anomalies through an interactive visualization tool, where they can annotate, modify, create, and remove events. Using these annotations, the framework leverages human knowledge to improve the anomaly detection pipeline. We demonstrate the usability, efficiency, and effectiveness of Sintel through a series of experiments on three public time series datasets, as well as one real-world use case involving spacecraft experts tasked with anomaly analysis tasks. Sintel's framework, code, and datasets are open-sourced at https://github.com/sintel-dev/.

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

As machine learning is applied more and more widely, data scientists often struggle to find or create end-to-end machine learning systems for specific tasks. The proliferation of libraries and frameworks and the complexity of the tasks have led to the emergence of "pipeline jungles" -- brittle, ad hoc ML systems. To address these problems, we introduce the Machine Learning Bazaar, a new approach to developing machine learning and AutoML software systems. First, we introduce ML primitives, a unified API and specification for data processing and ML components from different software libraries. Next, we compose primitives into usable ML programs, abstracting away glue code, data flow, and data storage. We further pair these programs with a hierarchy of search strategies -- Bayesian optimization and bandit learning. Finally, we create and describe a general-purpose, multi-task, end-to-end AutoML system that provides solutions to a variety of ML problem types (classification, regression, anomaly detection, graph matching, etc.) and data modalities (image, text, graph, tabular, relational, etc.). We both evaluate our approach on a curated collection of 431 real-world ML tasks and search millions of pipelines, and also demonstrate real-world use cases and case studies.