We've added 38 new metrics to Field Metrics Monitors. Check our docs page to see the full list of available metrics. Many of the newly released metrics are supporting manual thresholds only, and automated thresholds for them will be released soon.

In addition, we've introduced some categorization to help organize all the different field metrics:

• Uniqueness: these check for duplicates in unique keys like UUIDs, and for changes in cardinality
• Completeness: these check for ways that data can be null or otherwise unpopulated.
• Distribution: these check for shifts in the numeric profile of data
• Validity: check that values are honoring expected and usable formats, including common data entry errors.

Some large enterprises have more than one Monte Carlo environment. A subset of their users and admins need to frequently traverse between their multiple accounts. This used to be a cumbersome process to log out and log back in, but has been made much easier.

All the accounts for which an email address is a user are now presented in the top-right dropdown, allowing the user to easily switch between accounts.

Monte Carlo now supports deploying a remote Agent in Azure for customers to establish connectivity between Monte Carlo and their resources in Azure. See some key benefits of the agent architecture here.

And get started with the docs or reach out to Monte Carlo to deploy using the Azure Agent!

We’ve made a change to the list of Incident Statuses for the first time in a while! We've added Work in progress, which makes it easy to indicate that an Incident is a real issue and is being worked on.

Connecting Pinecone to Monte Carlo allows for monitoring the most critical component of your AI pipelines. We've brought Monte Carlo's data anomaly detection to Pinecone by observing patterns in Vector Count by Index and Index Namespace.

As soon as you connect Pinecone, hourly tracking of Vector Count by each Index and Index Namespace will be cataloged to be view and actively monitored by Monte Carlo's machine learning - no other setup necessary. You can see the expected Thresholds of Vector Count highlighted on the chart as well.

Learn how to setup Pinecone with Monte Carlo in the documentation. More monitors and metadata about your Pinecone assets are coming soon.

The Incidents and Insights dashboard has been renamed Insight Reports, where all CSV reports are available for download.

Monte Carlo can now integrate with multiple Slack environments. Historically, you could only connect with a single Slack workspace, which was a challenge for large organizations had different slack workspaces per business unit. Now, you can connect multiple workspaces, allowing you to send the right notifications to the right people.

Once multiple Slack integrations are configured, the user can select which integration to use when adding recipients to an audience.

It is now possible to deploy Comparison Rules using Monitors as Code. When this new monitor type was initially released in November, configuration was only supported via the UI. With Monitors as Code, they can more easily be deployed at scale.

While PagerDuty has been a long-supported integration for years, it was not included as a recipient type in Notifications as Code. Until now!

PagerDuty is now supported as a recipient type in Notifications as Code 2.0.

Monte Carlo now supports the new agent architecture on AWS with both CloudFormation and Terraform!

Key Benefits of the new architecture include:

• Simplicity: the agent has far fewer resources and components than before.
• Transparency: the code and templates are all publicly available for easy review with an audit and change log.
• Faster & Fewer updates: get improvements to Monte Carlo more quickly, but also with fewer manual upgrades.
• Flexibility: support the customizations you needed.
• More reliability: reachability heartbeats + faster deployments.

Learn more in our docs!