IoT devices and technologies are very popular now. Therefore, an important task is to ensure the smooth operation of IoT infrastructures. In this article, we will learn why and how to monitor MQTT metrics and also look at the tools that you should use for monitoring.

The explosion of talent available for remote work (and the widespread acceptance of remote first employment) allows for global collaboration on an unprecedented scale.

Healthcare providers must be extremely vigilant in their cybersecurity defense posture. After all, vulnerabilities in the Internet of Medical Things (IoMT) cost hospitals nearly $21 billion in 2021. New security discoveries by Ivanti partner Cynerio recently made that statistic personal for many providers. While working with an existing healthcare customer, Cynerio found five zero-day vulnerabilities in Aethon TUG autonomous robots – an IoMT device found in many healthcare facilities.

I was intrigued by two recent IoT related survey and reports. They emphasise use cases about efficiencies and return on investments but, surprisingly, there is no mention of people-safety nor productivity. I think it is imperative to place people at the heart of the IoT universe. Today, people related use cases have been greatly understated. We will be doing a great injustice if we continue to ignore the human angle.

There has been a lot of coverage of machine learning (ML) for biological research, for radiology, and for other uses where the direct users are academics, researchers, and medical professionals. However, there is an opportunity for some biological information to be useful in the retail industry. One area is in skincare.

We’ve already outlined why API performance matters and what aspects of APIs to test, but what is the difference between API testing and monitoring? As with most things, context matters. The use cases for testing and monitoring are different because the objectives are different. The ultimate goal is to verify that your APIs are functioning properly, but staging environments vary significantly from production environments.

In 2019, Alexander Mann was working in the microchip industry, putting in 12-hour shifts that left no time for him to tend to his large vegetable garden. “I started looking for ways that I could remotely water or check on my plants,” he says. Products that could help him were either too costly for a hobby gardener or required special internet connections, so Mann decided to learn about IoT and create his own setup.

MLOps pipelines are a set of steps that automate the process of creating and maintaining AI/ML models. In other words, Data Scientists create multiple notebooks while building their experiments, and naturally the next step is a transition from experiments to production-ready code. The best way to do this is to build an effective MLOps pipeline. What’s the alternative, I hear you ask? Well, each time you want to create a model, you run your notebooks manually.

The industrial revolution was a watershed period in human history. The shift from piecemeal, cottage-industry work to mechanized manufacturing transformed the way humans work. Since the 18th century, successive waves of innovation, such as the assembly line and the computer, continued to alter and change the nature of manufacturing. Today, we find ourselves in the midst of another industrial transformation.

Aiven's OSPO is now one year old. Find out what OS Engineering Manager Josep Prat and his team have learned about setting up and running the function.