Operations | Monitoring | ITSM | DevOps | Cloud

We're excited to announce that the Splunk Machine Learning Toolkit (MLTK) version 5.2 is available for download today on Splunkbase! Earlier this month, I discussed how the release of version 5.2 will make machine learning more accessible to more users. Splunk’s MLTK lets our customers apply machine learning to the data they're already capturing in Splunk, develop models, and operationalize these algorithms to glean new insights and make more informed decisions.

NiCE is introducing a new line of monitoring solutions for Microsoft Azure. The kick-off for the new Azure monitoring solutions will be the NiCE Active 365 Monitor for Azure, a brilliant monitoring set for Microsoft Teams, SharePoint, OneDrive and Office Platform hosted on Microsoft Azure Monitor. Get a sneak preview on how the new NiCE Active 365 Monitor will boost your efficiency in Azure performance and availability monitoring.

We are happy to announce that ManageEngine has been recognized by Gartner in its 2020 Magic Quadrant for Application Performance Monitoring. Applications Manager has helped thousands of customers across the globe streamline and manage their servers and applications effectively.

We are happy to announce that our widely popular free Ping Management Pack just got a community dashboard for SquaredUp. The dashboard is now available for download at SquaredUp Community site. The Ping Management Pack Dashboard includes one dashboard and one perspective. We would love to hear your feedback and listen to improvements to get this even better in the future.

We are thrilled to announce that Grafana 7.0 has been released for general availability. Join us as Torkel Ödegaard, the creator of Grafana, hosts a full demo of 7.0 during GrafanaCONline today. With Grafana v7.0, our goal was to extend on the Grafana platform by making it easier and more consistent for existing users, and intuitive and simple for those not familiar with Grafana.

For teams that deal with machine learning (ML), there comes a point in time where training a model on a single machine becomes untenable. This is often followed by the sudden realization that there is more to machine learning than simply model training. There are a myriad of activities that have to happen before, during and after model training. This is especially true for teams that want to productionize their ML models.