Follow the recent outages and downtime for DataRobot MLOps in the table below.
Start Time |
Type |
Length |
Message |
Details |
|||||
---|---|---|---|---|---|---|---|---|---|
February 20, 2024 18:34 UTC |
WARN |
about 7 hours |
Delay in Deployment Alerts |
See more
|
|||||
February 20, 2024 18:24 UTC |
WARN |
5 minutes |
Partially Degraded Service |
See more
|
|||||
February 13, 2024 02:34 UTC |
MAINT |
about 4 hours |
DataRobot - US Managed AI Cloud Maintenance |
See more
|
|||||
September 18, 2023 19:49 UTC |
WARN |
about 5 hours |
Network Latency/Timeout noticed in Kubeworkers |
See more
|
|||||
August 25, 2023 15:29 UTC |
WARN |
less than a minute |
Deployment report generation failure in US and EU Prod |
See more
|
|||||
2024-04-26 16:21:19 UTC UTC |
STATUS |
? minutes |
Sign in to view more status history. |
See more | |||||
2024-04-26 16:21:19 UTC UTC |
STATUS |
? minutes |
Sign in to view more status history. |
See more | |||||
2024-04-26 16:21:19 UTC UTC |
STATUS |
? minutes |
Sign in to view more status history. |
See more | |||||
2024-04-26 16:21:19 UTC UTC |
STATUS |
? minutes |
Sign in to view more status history. |
See more | |||||
2024-04-26 16:21:19 UTC UTC |
STATUS |
? minutes |
Sign in to view more status history. |
See more |
Sign Up for More DataRobot MLOps History
StatusGator has about 2 years of DataRobot MLOps status history.
DataRobot MLOps status, last 24 hours:
Up: 24 hours
Warn: 0 minutes
Down: 0 minutes
Maintenance: 0 minutes
We've been monitoring DataRobot MLOps outages since February 3, 2022.
Here's the history of service outages we've observed from the DataRobot MLOps Status Page:
DataRobot MLOps is a Cloud Infrastructure solution that StatusGator has been monitoring since February 2022. Over the past about 2 years, we have collected data on on more than 142 outages that affected DataRobot MLOps users. When DataRobot MLOps publishes downtime on their status page, they do so across 29 components and 4 groups using 4 different statuses: up, warn, down, and maintenance which we use to provide granular uptime metrics and notifications.
Many StatusGator users monitor DataRobot MLOps to get notified when it's down, is under maintenance, or has an outage. We've sent more than 200 notifications to our users about DataRobot MLOps incidents, providing transparency and peace of mind. You can get alerts by signing up for a free StatusGator account.
If DataRobot MLOps is having system outages or experiencing other critical issues, red down notifications appear on the status page. In most cases, it means that core functions are not working properly, or there is some other serious customer-impacting event underway.
Warn notifications are used when DataRobot MLOps is undergoing a non-critical issue like minor service issues, performance degradation, non-core bugs, capacity issues, or problems affecting a small number of users.
DataRobot MLOps posts separate notifications for planned maintenance work. StatusGator will notify subscribers when DataRobot MLOps enters a pre-planned maintenance window, keeping you up to date.
Since DataRobot MLOps publishes a feed of proactive maintenance events on their status page, StatusGator will collect information about these events. Maintenance events for all your services can be viewed within StatusGator as a unified feed.
When DataRobot MLOps posts issues on their status page, we collect the main headline message and include that brief information or overview in notifications to StatusGator subscribers.
When DataRobot MLOps has outages or other service-impacting events on their status page, we pull down the detailed informational updates and include them in notifications. These messages often include the current details about how the problem is being mitigated, or when the next update will occur.
Because DataRobot MLOps has several components, each with their individual statuses, StatusGator can differentiate the status of each component in our notifications to you. This means, you can filter your status page notifications based on the services, regions, or components you utilize. This is an essential feature for complex services with many components or services spread out across many regions.
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