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.

Your applications most likely consist of multiple components. These components could be written in different languages, with each individually instrumented with Sentry’s SDK. The goal of our tracing solution is to make sure developers get a full picture of the data captured within their tech stack. Tracing allows you to follow a request from the frontend all the way to your backend application and back.

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.

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.

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.

I was intrigued by two recent IoT related survey and reports. They emphasize 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.

Debugging JavaScript is an inevitable part of web development, and not the nicest one. Debugging jobs always seem to pop up when you’re already buried under piles of work, and a teammate pings you about an issue that was overlooked in testing and has been causing frustrations since your last release. That’s why it helps to be prepared for that eventuality, and equipped with the developer tools to help you debug faster.