October 2020 Summaries

Arize AI has been selected for insideBIGDATA's Impact 50 list in Q4 2020, a quarterly list of the most important movers and shakers in the big data industry. Arize AI is recognized as the leading ML Observability platform, designed to troubleshoot, monitor, and explain AI deployed in the real world. The company was founded by leaders in the Machine Learning space to bring better visibility and performance management over AI. With its innovative platform, Arize AI aims to create a more transparent and trustworthy future with AI, enabling Data Scientists and Machine Learning engineers to deploy models with confidence.

Arize AI has won the 2020 AI TechAward for Enterprise AI, recognizing its contribution to technical innovation in the AI, Machine Learning & Data Science industry. The company's product/technology was selected based on notable attention and awareness in the industry, general regard and use by the developer & engineering community, and being a leader in its sector for innovation. Arize AI is focused on providing an ML Observability platform to help make machine learning models work effectively in production.

Statistical distance metrics are used to quantify the distance between two distributions, which is extremely useful in machine learning observability. Data problems can arise from sudden data pipeline failures or long-term drift in feature inputs, and statistical distance measures provide teams with an indication of changes in the data affecting a model and insights for troubleshooting. Real-world examples include incorrect data indexing mistakes, bad text handling, and software engineering changes that alter the meaning of a field. These issues can be caught using statistical distance checks on model inputs, outputs, and actuals, which allow teams to get in front of major model issues before they affect business outcomes. The reference distribution can be fixed or moving, depending on what is being tried to catch, and different types of distance checks are valuable for catching different types of issues. The PSI metric is a great example of a statistical distance measure with real-world applications in the finance industry, particularly for detecting changes in feature distributions that might make them less valid as inputs to models.

Paperspace customers can now easily integrate the Arize AI platform for model monitoring, troubleshooting, and explainability, allowing them to gain insights into their models' performance in production and troubleshoot issues quickly. The integration provides a simple pre-tested solution that is easy to set up, enabling teams to monitor data drift and model drift, and troubleshoot problems in a purpose-built platform designed for ML Observability. Arize AI's platform helps teams transition from research environments to production, maintaining the results delivered, and builds trust between research teams and end users by providing explainable and transparent insights into their models' performance.

Arize introduces Nate Mar as their newest team member, who will be joining the engineering department. Previously at PagerDuty, Nate worked on machine learning infrastructure and alert notification pipelines. He is passionate about ML fairness and transparency, aligning with Arize's mission to help engineers and data scientists better understand model performance in production. In his personal life, Nate holds a degree in Political Science from UC Berkeley but discovered his passion for software engineering after starting his career in tech. When not coding, he enjoys playing the cello, learning Chinese, and reading cookbooks.