Machine learning has become an important component of many applications we use today. And adding machine learning capabilities to applications is becoming increasingly easy. Many ML libraries and online services don’t even require a thorough knowledge of machine learning. However, even easy-to-use machine learning systems come with their own challenges. Among them is the threat of adversarial attacks, which has become one of the important concerns of ML applications.

The Internet of Things (IoT) ecosystem is expanding at a rapid rate, with the number of devices growing every year. The increase in physical hardware being manufactured multiplies the amount of software needed to perform various functions on new platforms. There are a range of IoT use cases, including voice-controlling the lights in your home, monitoring air pollution, or the parking feature of a self-driving car.

A broad enthusiasm for AI is already evidenced by the technology’s adoption within many organizations in 2021. In a recent multinational survey conducted by IDG, we found that more than one-quarter (27%) of the survey respondents have fully deployed AI-enabled ITSM/ITOM solutions, and another 34% have made initial deployments in select use cases and departments. At the same time, another 32% are exploring AI-based solutions or gathering information about them.

With our latest Android app update (3.1., build 242) you will never miss a critical SIGNL4 alert again. Even if your phone is muted or in do-not-disturb mode, SIGNL4 can now make a lot of ‘noise’ and wake you up reliably when a major or critical incident occurs. Here is how it works….

One of the experiences I’ve truly enjoyed over my first year as a senior solutions engineer here at Grafana Labs has been learning from our community and customers about their own Grafana journeys. I’ve been impressed by some remarkable dashboards for home automation, personal health data visualizations, family Minecraft statistics, and energy usage projects.