SAN JOSE, Calif., Sept. 14, 2023 (GLOBE NEWSWIRE) — Aporia, the leading Observability platform for AI products, today announced a partnership with Snowflake, the Data Cloud company, designed to enhance performance by providing AI models and data observability in the cloud. Integrating Aporia’s ML observability platform with the Data Cloud not only promotes the efficiency and effectiveness of data science, but also helps to expand the adoption of responsible AI.
Aporia’s observability platform now seamlessly integrates with Snowflake, enabling users to monitor their data and production machine learning (ML) models at scale. The integration extends to features within Snowflake such as Snowpark, the runtimes and libraries that make deployment and processing of non-SQL code in Snowflake simpler, faster, and more secure. Together, joint customers can monitor billions of predictions to uncover insights from production data and improve their ML models’ performance. In addition, Aporia’s privacy-first mode ensures that no sensitive data leaves a user’s Snowflake environment. Aporia supports all AI/ML use cases and every model type including Tabular, NLP, LLM, and Computer Vision within the Snowflake platform.
In sensitive domains, gaining insight into the reasons behind specific predictions made by AI projects and the ML models driving them is critical. However, the lack of explainability can hinder the identification of biases, the assurance of fairness, and establishing trust in models. Aporia’s integration with Snowflake addresses these concerns by enabling users to monitor ML models within minutes. It facilitates near real-time detection for issues like AI hallucinations, drift, bias, data integrity issues, model decay, and performance degradation.
Aporia’s platform centralizes model management, equipping teams with the tools to effectively monitor each of their models and quickly investigate any production alerts. Teams are also able to utilize customizable dashboards and metrics to gain a clear view of their model’s health. Additionally, Aporia facilitates prompt resolution of production issues and AI hallucinations through collaborative analysis and provides diverse analytics for a more nuanced understanding. The company offers an all-in-one root cause analysis tool that 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 within a unified monitoring platform.
“As we continue to enable organizations to champion responsible AI and unlock its full potential with unparalleled insights, enhanced performance and monitoring capabilities, we are thrilled to announce our partnership with Snowflake,” said Liran Hason, CEO of Aporia. “By combining Aporia’s advanced AI observability into Snowflake’s secure, scalable infrastructure, we are not only addressing the crucial need for model observability to the Data Cloud, but also helping data-driven organizations accelerate their impact on data science, achieve new heights in performance, and drive overall business success.”
“We are thrilled to partner with Aporia to bolster the capabilities of the Snowflake Data Cloud and provide joint customers with advanced AI observability,” said Torsten Grabs, Senior Director, Product Management, Snowflake. “By equipping users with powerful tools to fully leverage their efforts and promote responsible AI usage, we are collectively advancing data science, leading to increased effectiveness, efficiency, and trustworthiness.”
For more information, visit https://www.aporia.com/
Aporia is the leading observability platform for AI products, recognized as a Technology Pioneer by the World Economic Forum for its mission of driving Responsible AI. The company is trusted by Fortune 500 companies and industry leaders – including Bosch, Lemonade, Armis, Munich RE, & Sixt – to monitor, visualize, control, and ensure AI products are responsible, fair, and high performing. Aporia empowers data scientists and ML teams to confidently monitor, visualize, and gain insights to improve models in production.