Operations | Monitoring | ITSM | DevOps | Cloud

Putting machines to work to enhance our everyday lives has been well-ingrained in our society for at least a couple of centuries now. IT workers use machine learning (ML) in their daily work routines, even if they don’t consciously realize it. Automated email alerts, issue escalations, and security patching are just a few examples of how ML has put the systems we rely on to work for us.

How do you determine whether one multifamily deal is better than another? Let me start by saying that a deal is only as good as the assumptions you’re making as an investor, and assumptions are not guaranteed to materialize. As a real estate investor, operator and syndicator through my company Blue Lake Capital, a significant part of our analysis when considering properties to invest in is looking at how the property performed in the past.

At Torq, we use gRPC as our one and only synchronous communication protocol. Microservices communicate with each other using gRPC, our external API is exposed via gRPC and our frontend application (written using VueJS) uses the gRPC protocol to communicate with our backend services. One of the main strengths of gRPC is the community and the language support. Given some proto files, you can generate a server and a client for most programming languages.

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.

Application Performance Monitoring (APM) tools are a critical component of distributed applications now. But choosing the right APM tool can be tricky. In this article, we go through a list of the top 21 APM tools including open-source APM tools which can help monitor and improve your application performance.

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….