New Releases, Enhancements, Changes + Arize in the News!
GraphQL Public API
The Arize GraphQL API offers a powerful interface for automating monitor setup, making bulk changes, and exporting configurations. It enables customers to integrate Arize with their internal systems for a repeatable and consistent setup. Use the API to:
- Bulk create custom monitors with complex filters or baselines
- Export existing monitor configuration, make edits, and bring edits back into Arize
- Build automation by integrating Arize with internal services
How to use GraphQL in the interactive explorer
Moving Custom Baselines on Drift Monitors
Customize your drift monitor baseline to identify changes specific to your monitor with parameters such as a fixed time range, a moving time range, different versions, and specific filters.
Drift Monitor With Custom Moving Baseline
Color by Prediction Label
We've added an additional coloring option when troubleshooting embeddings using the 2D and 3D UMAP visualization. Users can choose between the 'color by dataset', or the new 'color by prediction label' option, enabling teams to better analyze their unstructured data.
Duplicate Managed Monitor To Create Custom Monitor
Duplicate a pre-set (managed) monitor after the 'Set Up Model' flow to easily customize your monitor's details. Use the duplicate model to choose an alternative baseline, edit your threshold value, add alert recipients, and more.
With ML observability in place to quickly detect and diagnose the root cause of model performance degradation, America First Credit Union’s ML team now ships AI with confidence. Learn more in our newest case study.
Arize AI is incredibly excited to debut our embedding drift monitoring and embedding analysis product! This release enables teams to log models with both structured and unstructured data to Arize for monitoring. By monitoring embeddings, teams can proactively identify when their unstructured data is drifting. Troubleshooting is simple with interactive visualizations to help isolate new or emerging patterns, underlying data changes, and data quality issues.
AI can power phenomenal revenue growth – until it doesn’t. That lesson is often learned the hard way when problematic AI systems are not caught and remedied before materially impacting revenue. Here are four steps enterprises can take to avoid their next earnings call becoming a retrospective on AI gone awry.
Did you know that there are not one, not two, but three types of observability your system could need? In this piece, Arize CPO, Aparna Dhinakaran, and Bigeye co-founder & CEO, Kyle Kirwan, walk through the different types of observability and how you should use them.
Arize Founding Engineer and Head of Global Solutions Architecture, Gabe Barcelos, shows how you can quickly integrate Arize into your existing pipeline for real-time and scalable ML observability. This article dives into leveraging a simple Kafka consumer which consumes a micro-batch of incoming events and publishes them to Arize so you can observe your model in real-time.
Stefano Goria is the Co-Founder and Chief Technical Officer (CTO) of Thymia, a company aiming to make mental health assessments faster and more accurate through an approach that combines video games based on neuropsychology with analyses of facial microexpressions and speech patterns. In this wide-ranging Q&A, Goria talks about the company’s AI strategy, subjectivity and biases in mental health data, and the unique ethical concerns of applying AI in the mental health field.