New Releases, Enhancements, Changes + Arize in the News!
Embedding Drift Monitors
Use embeddings drift monitors to receive automatic alerts when your unstructured data drifts. Embedding drift is measured by calculating the Euclidean Distance between embedding vectors.
JS distance is a symmetric derivation of KL divergence, and it is used to measure drift. Use JS distance to compare distributions with low variance to measure the similarity between two distributions.
Programmatically create, query, and delete jobs & files using our public facing GraphQL API. Our GraphQL API provides a direct path between cloud storage and the Arize platform to easily set up jobs and automate job creation.
Model typeparameter field is now required (previously optional).
Model versionis now optional (previously required). If you do not set a model version, it will appear as “no_version” in the UI.
Schematype is now imported from
- Added parameter
environmentto the real-time logger. This allows to log in real-time training/validation records.
environmentparameter is required.
Recently, Arize co-hosted an event with Vectice featuring the “Voices of ML Leaders.” Miss it? Here are four takeaways from leaders at Microsoft, Kohl’s, and Yelp on how to lead effective ML teams:
- 1.Tie Model Metrics To Business KPIs Upfront
- 2.Invest All the Way Through the ML Lifecycle
- 3.Consider Threading the Needle With Central ML
- 4.Assess New Talent By Simulating Real-World Problems
Arize and Ray are partnering to help teams better productionize ML for scale and usability! Learn more and about Ray’s distributed ML framework and Arize’s ML observability platform and follow along with a code example that shows the scaffolding of both technologies working in tandem in this blog by Dat Ngo, Arize Solutions Architect.
The increasing reliance on AI systems means model monitoring needs to go the extra mile and encompass scale as a priority. For Arize, this means ML monitoring must accommodate hundreds or even thousands of models with thousands of features – all with unique requirements and little human intervention.
Aman Khan, Group Product Manager at Arize covers how Arize's next generation of model monitoring is helping teams catch issues in production sooner with less oversight through automation, programmatic monitoring access, and native alerting integrations with Pagerduty and OpsGenie.