09.26.2022
New Releases, Enhancements, Changes + Arize in the News!
What's New
Alerting Integration: Direct Monitor Setup
Set up an alerting integration directly from the 'Edit Monitor' page to streamline your monitoring workflow. This enables increased flexibility to configure an integration specific to your alerting needs.
Alerting Integration: Slack
Receive monitoring alerts through Slack with our native Slack integration. Send your monitoring alerts through Slack to:
Streamline your monitoring workflow per model or individual monitor
Reduce time to resolution with custom model dimensions and enhanced metadata
Stay organized within your system and across multiple stakeholders
Learn how to use our Slack integration here.
Use our public GraphQL API to programmatically configure your alerting integration within your infrastructure. Learn more here.
Data Quality Metric: P99.9
Use P99.9 to gain a granular understanding of your model's dimensions. Navigate to any data quality monitor within the 'Monitors' tab to edit your evaluation metric.
Enhancements
Drift Monitor: Delay Evaluation
Accurately evaluate drift on delayed data by configuring your 'Delay Evaluation By' setting. Navigate to the 'Edit Monitor' page under 'Custom Settings' to pick from a wide breadth of time windows relevant to your data.
Learn more about an evaluation window here.
In The News
Arize is now available on Google Cloud Marketplace. This availability marks the expansion of the company’s partnership with Google Cloud, which will help Arize deliver its platform – tracking billions of model predictions daily – to more customers globally.
Learn how to use Hugging Face and Arize to ship NLP sentiment classification models with confidence. Dive into how to ingest embedding data on Arize and how to look at embedding drift.
Recently, Arize hosted Monte Carlo’s co-founder and CTO in a session on “The Evolution of the Data Stack.” Miss the event? Here are six top takeaways...
Debates on the ideal team structure for ML organizations are heating up, from Tecton’s recent piece arguing that “centralized machine learning teams fail” to Meta’s recent embrace of a decentralized approach. This article urges teams not to throw in the towel on centralized machine learning (ML) teams yet — offering a blueprint for when to embrace the approach and how to get it right.
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