Aporia Launches First Ever Root Cause Analysis Tool for Real-Time Production Data Investigation

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Aporia, a leading ML Observability platform, announced the launch of Production IR (Production Investigation Room), the first of its kind tool that is radically distinguished by its intuitive ease of use, redefines the process of investigating production data. This all-in-one root cause analysis tool provides data scientists, ML engineers and analysts with a seamless and easy to navigate digital environment for real-time data analysis, root cause investigation and deep insights, all within a unified monitoring platform.

Historically, investigating production data has been complex and time-consuming, hindered by limited collaboration and code changes. Aporia’s Production IR simplifies these complexities and serves as a comprehensive solution for data professionals, streamlining the investigation process with its intuitive, user-friendly and customizable interface that resembles a notebook. By eliminating the need for extensive coding, Aporia’s platform empowers stakeholders to delve into their production data, gain valuable insights, and improve root cause analysis (RCA) while enhancing ML model performance.

“By providing quick access and insights to production data, Production IR changes the game for investigating ML events and anomalies,” said Liran Hason, CEO of Aporia. “Data scientists and engineers now have a fast and effortless way to extract valuable information from their production code with a simple click of a button. Our goal is to enable an innovative and effective root cause analysis process that allows users to swiftly understand the factors impacting their model’s performance.”

The analysis tool is designed with accessibility in mind for users of all levels. It offers high customizability to suit specific needs, and can be easily configured to accommodate different datasets and requirements, facilitating a seamless visualization of investigations. Aporia handles the heavy lifting when it comes to managing Big Data, freeing users from the burdens associated with large-scale production model/data analysis. Additionally, the highly collaborative nature of Production IR encourages knowledge sharing, enabling users to easily compare analyses and share insights within the Aporia platform.

Production IR offers a range of powerful features to facilitate effective investigation, including segment analysis, data statistics, drift analysis, distribution analysis, and Incident Response. The ability to provide Incident Response plays a crucial role in ensuring the robustness and productivity of AI products by providing decision makers with the confidence that issues or threats are dealt with effectively. By incorporating Incident Response into AI practices, organizations can address any potential challenges and practice and maintain responsible and ethical AI deployment. The tool also boasts an impressive embedding projector capability that allows users to visualize unstructured data in both 2D and 3D using UMAP dimension reduction. This feature helps users identify different clusters within the data and uncover underlying patterns within each cluster. It is suitable for NLP (Natural Language Processing), LLM (Large Language Models), and CV (Computer Vision) models, providing a holistic understanding of the production data and driving impactful improvements in ML models.

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