Aporia launches root cause analysis tool for real-time production data analysis

Aporia, a machine language (ML) observability platform, today announced the launch of a tool that aims to facilitate the analysis of manufacturing data. The company says its Production Investigation Room (Production IR) tool provides data scientists, ML engineers and analysts with a “first-of-its-kind” unified monitoring platform that offers a digital environment for real-time data analysis, cause main investigations and insights.

Traditionally, manufacturing data analysis has been complex and time-consuming, hampered by limited collaboration and code changes.

Aporia says the new tool simplifies the process with a user-friendly and customizable interface that resembles a notebook. This should eliminate the need for extensive coding and help stakeholders derive valuable insights from their production data.

“Production IR provides centralized access for AI/ML production data analysis. (It) eliminates the challenges and pains of traditional methods, such as limited data access, limited collaboration, and the need for extensive coding,” Liran Hason, cofounder and CEO of Aporia, told VentureBeat. Aporia’s direct connection to the user database (DDC), enables fast and efficient access to big data, simplifying the management of large data sets.”

Hason emphasized that centralized views of manufacturing data foster collaboration and accelerate root cause analysis (RCA).

He argues that this approach improves ML model performance and improves the efficiency and effectiveness of data exploration. The platform also allows investigators to leave notes, report progress, and alert others to specific issues, facilitating collaborative investigations.

According to Aporia, the new offering offers high customization to meet specific needs and can be easily configured to meet different datasets and requirements, allowing for easy visualization of surveys.

Additionally, Production IR automatically configures big data queries, alleviating the challenges associated with large-scale production models and data analytics.

The company said the collaborative nature of the new tool promotes knowledge sharing among users. Enables analytics comparison and facilitates sharing of insights within the Aporia platform.

“ML engineers and data scientists can leverage its capabilities to create interactive dashboards that can be shared and integrated with favorite tools like Databricks, Snowflake, and more,” added Aporia’s Hason. “(With) a unified view of data and insights, everyone on the team can access the same information.”

Streamline root cause analysis through unified data tracking

Hason pointed out that traditional root cause analysis (RCA) relies on extensive coding, which consumes resources, causes delays, isolates information and increases the potential for human error. Furthermore, RCA is typically associated with high costs.

“Production IR overcomes these challenges by providing insights to improve models. (It) offers customization options and provides an engaging experience for data scientists and engineers, fostering collaborative investigation,” he explained. “This leads to accelerated mean time to resolution (MTTR) and simplifies the RCA process while improving speed response and agility while reducing the number of resources invested in activities”.

With a wide range of analytics capabilities, Production IR aims to streamline data investigation, including segment analysis, data statistics, drift analysis, distribution analysis, and incident response.

“Aporia’s segment analysis feature allows investigators to break their data into smaller, more manageable segments. This allows for granular examination of specific subsets of data, which can help identify patterns, anomalies or correlations that may not be apparent when examining the data as a whole,” said Hason. “Our platform’s new capabilities provide investigators analytical skills that enable them to conduct more efficient and effective investigations.”

Responsible and ethical, reliable and efficient AI

Aporia says the tool’s incident response capability improves the reliability and efficiency of AI products, enabling decision makers to effectively address problems or threats. The company said organizations can proactively address potential challenges by integrating incident response into AI practices and ensuring responsible and ethical implementation of AI.

Additionally, the tool incorporates a built-in projector, which allows users to visually represent unstructured data in 2D and 3D using UMAP size reduction.

“An embedded projector is a tool that helps users view and explore complex unstructured data, such as text or image data, in a lower dimensional space, usually 2D or 3D views,” said Hason. “It uses a size reduction technique called unified manifold approximation and projection (UMAP). This can be easily observed in the built-in projector view.

Hason said the feature is significant for NLP, LLM and CV models, as it provides a comprehensive understanding of manufacturing data and drives improvements in ML models.

He explained that the built-in projector analyzes the spatial arrangement, proximity and geometric relationships of data points to discover patterns within the data. These models expose underlying structures, trends, or associations that may not be immediately apparent in the original high-dimensional data.

“By leveraging an embedded projector with UMAP, users also gain a deeper understanding of their unstructured data, enabling tasks such as data analysis, model interpretation, feature engineering, and hypothesis generation across NLPs, LLMs and CVs,” Hason told VentureBeat.

What is the future of Aporia?

Hason said Aporia aims to democratize and accelerate the use of artificial intelligence, enabling companies to establish trust and ensure safe use. He emphasized that the consequences of AI mistakes can range from mere inconveniences to potentially life-altering impacts.

“Imagine if the AI ​​system in the healthcare industry misdiagnoses a patient’s condition or if a financial forecasting model fails to accurately predict market trends. The repercussions can be serious. It is therefore crucial to ensure that AI systems are not only effective, but also reliable, understandable and trustworthy,” she said.

Hason said Aporia is dedicated to assisting enterprises in achieving responsible AI through its ML observability platform. He emphasized that the platform enables transparency by offering clear insight into AI decision-making, fostering user trust, and accelerating AI adoption.

“At Aporia, our primary goal is to ensure and enable responsible AI for every individual around the globe. We are dedicated to building a platform that provides an end-to-end solution for businesses to manage their AI systems responsibly and effectively,” he said. “Our commitment goes beyond just building technology; it’s about creating a safe and trusted environment for AI to be used across industries.”