Operations | Monitoring | ITSM | DevOps | Cloud

In the early 2010s, Apache Hadoop captured the imagination of the tech community.

We’re thrilled to announce that Mattermost has earned the 12th annual DEVIES Award for Best Innovation in ITOps! The award — given to a platform “responsible for acquiring, designing, deploying, configuring, and maintaining the physical and virtual components that comprise IT infrastructure” — was presented on Feb. 21 during DeveloperWeek 2024 to our very own Director of Product Marketing Amanda Cheong and Developer Advocate Andrew Zigler at the Oakland Marriott City Center.

In an era marked by the convergence of Big Data, hybrid cloud use, and the rise of machine learning and artificial intelligence, organisations across the Asia-Pacific (APAC) region find themselves at a critical juncture. The ability to collect, manage, and leverage data effectively is now a determining factor for competitive advantage – and will become more competitive.

Cloud infrastructure scaling can assist in rightsizing cloud resources in various ways. When discussions occur regarding scaling, its context can often refer to adding resources. However, it can also refer to removing resources. Preparing for how your cloud infrastructure scales begins with thought-out planning, design, and management of resource and tool allocations. This can save your organization time and costs that could otherwise negatively affect your employees, customers, and company.

Sift is a machine-learning-powered diagnostic feature in Grafana Cloud that SREs and DevOps teams can use to automate routine parts of incident investigation, such as searching for new errors in logs, surfacing recent deployments, or identifying overloaded Kubernetes nodes. We want Sift to springboard you into an investigation, so useful context is already there by the time you see an alert or declare an incident.

With Ubuntu confidential AI on Azure, businesses can undertake various tasks including ML training, inference, confidential multi-party data analytics, and federated learning with confidence. The effectiveness of AI models depends heavily on having access to large amounts of good quality data. While using publicly available datasets has its place, for tasks like medical diagnosis or financial risk assessment, we need access to private data during both training and inference.

With Gartner estimating that over 90% of newly created business software applications will contain ML models or services by 2027, it is evident that the open source ML revolution is well underway. By adopting the right MLOps processes and leveraging the lessons learned from the DevOps revolution, organizations can navigate the open source and proprietary ML landscape with confidence.