New Releases, Enhancements, Changes + Arize in the News!

What's New

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.

Learn how to create an embeddings drift monitor here.

Drift Monitor Metric: JS Distance

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.

Learn how to use and calculate JS Distance here.

File Importer API

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.

Walk through a step-by-step tutorial here.

Interested in using the file importer API? Reach out to support@arize.com for access.


Python SDK 5.0.2

  • Removed bulk_log(). Learn how to log data to Arize here.

  • Deprecated CATEGORICAL & BINARY models.

  • Model type parameter field is now required (previously optional).

  • Model version is now optional (previously required). If you do not set a model version, it will appear as “no_version” in the UI.

  • Schema type is now imported from arize.utils.types.

  • Added parameter environment to the real-time logger. This allows to log in real-time training/validation records.

    • The environment parameter is required.

In The News

Four Crisis-Tested Lessons For Leading Effective ML Teams

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

Ray + Arize: Productionize ML for Scale and Usability

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 Next Generation of Machine Learning Monitoring

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.

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