Operations | Monitoring | ITSM | DevOps | Cloud

In the software industry, monitoring and observability come hand in hand and are referenced simultaneously, but both are not the same thing. Having the right tools for these capabilities is vital to keep your systems, data, and security perimeter safe. With cloud solutions topping up in the integration space, great tools have been available in the market, and choosing the right one would be challenging.

Application engineers play a crucial role in bridging customer needs and the efforts of application development teams. Applications are becoming more complex—businesses are seeking to deliver more services while the software engineering components underlying those them are expanding and interacting in new ways. This combination of factors is driving the need for application engineers who can understand both the business and the technical aspects of applications.

Find out how to effectively and easily monitor and troubleshoot Dnsmasq for DHCP using Netdata.

Find out how to effectively and easily monitor and troubleshoot Dnsmasq DNS Forwarder using Netdata.

Your employees and customers are constantly on the move and rely on their mobile devices to stay connected and productive. On-demand remote support is crucial when technical issues become a roadblock in their day. With mobile device remote support, your IT team can troubleshoot problems quickly and efficiently from a tablet, computer, or smartphone.

It should surprise no one that Kubernetes uptake is growing and will continue to do so. The wildly popular container orchestration platform’s continuous development is fueled by broad adoption. This will continue in 2023 as more companies, teams and individuals embrace it as a platform for innovation, building new applications and scaling existing ones faster than ever before.

Find out how to effectively and easily monitor and troubleshoot CoreDNS using Netdata.

MLOps is the short term for machine learning operations and it represents a set of practices that aim to simplify workflow processes and automate machine learning and deep learning deployments. It accomplishes the deployment and maintenance of models reliably and efficiently for production, at a large scale. MLOps is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from data gathering to governance and monitoring.