Operations | Monitoring | ITSM | DevOps | Cloud

82% of organizations plan to increase their use of Service Level Objectives (SLOs), with 95% reporting that SLO adoption drives better business decisions, according to the Nobl9 2023 State of SLOs report. The traditional manual management of SLOs often results in inefficiencies and human errors, hindering productivity. Automating SLO management transforms these processes, enhancing accuracy and operational efficiency.

We believe in expanding our arsenal to accommodate your evolving needs. That’s why we are thrilled to announce that Applications Manager now provides wider performance monitoring support for Azure! The latest version of ManageEngine Applications Manager extends its monitoring support to 11 additional Azure services, empowering you to gain deeper insights into the health and performance of your entire cloud environment. Let’s take a look at the newly supported services.

Applications Manager now supports Azure security services, container services, integration services, storage sync services, app service plans, and more.

Most organizations migrate to the Amazon Web Services (AWS) public cloud from on-premises data centers. However, others have switched from another Infrastructure-as-a-Service (IaaS) platform to AWS. The most common reason we hear for choosing AWS is cost savings. As the largest public cloud provider, AWS leverages economies of scale, making its cloud services more affordable than those a company would have to build itself. Yet, not every organization can claim that AWS has saved it money.

Arguably the most important part of an on-call product is knowing that you will be notified when things break, wherever you are. When it comes to SMS and phone call notifications, we have to leave the familiar realm of the internet and JSON responses, and deal with systems that provide limited observability and insight into what’s gone wrong.

When working with ML models, it’s fair to say that a model is only as good as the data it was trained on. Training and testing models on quality datasets of an appropriate size is essential for model performance. Because of the intricate link between a model and the data it was trained on, it’s also important to be able to store datasets and versioned models together.

There’s no shortage of new developer tools promising to reduce toil and improve productivity. But investing in the latest technology can require a leap of faith organizations can no longer afford to make. Leaders need assurance that the investment they’re making will pay off, quickly.