Here at incident.io, we're always pushing the boundaries of incident management and seeking new ways to enhance your on-call experience. That's why we're excited to announce our latest feature that's set to level-up the way you respond to incidents.

With more developers working remotely, traditional code reviews have begun to shift. Classic water cooler conversations have turned into pings on Slack, and collaborative office spaces have transformed into stand-alone home setups. Remote work clearly has many advantages, but it can also leave developers feeling isolated. Asynchronous communication introduces massive bottlenecks for efficient feedback and creative brainstorming, particularly during code reviews.

I had the pleasure to be invited by Canonical’s AI/ML Product Manager, Andreea Munteanu, to one of the recent episodes of the Canonical AI/ML podcast. As an enthusiast of automotive and technology with a background in software, I was very eager to share my insights into the influence of artificial intelligence (AI) in the automotive industry.

Automated incident management is the process of automating incident response to ensure that critical events are detected and addressed in the most efficient and consistent manner. In incident management, time is of the essence and the primary benefit of automated incident management is speed. With automation, you can accomplish time-consuming tasks much quicker. This brings down the incident response time and allows the team to focus their attention on matters that require their expertise.

When your organization faces outages, errors, security breaches, and other incidents, you need to have a plan in place to take appropriate actions as needed. However, you also need a capable team of experts filling critical roles and responsibilities to execute those actions and effectively collaborate to resolve issues quickly. An incident response team, therefore should be developed in a way that avoids skills gaps in expertise.

In most engineering organizations, everyone agrees that in complex systems, failure is inevitable. It’s possible to prevent the recurrence of certain incidents, reduce their impact, or shorten the time to resolution. However, it’s impossible to avoid them altogether. In the past, we asserted failures are a result of people’s mistakes. It was all about “the bad apple theory,” focused on finding the “guilty party” and removing them to prevent future failures.

While many of us have already heard of Kubernetes, or may even be leveraging it within our technology stacks, it’s still important to remember that the platform is undergoing massive adoption and evolvement. Due to its relative infancy, Kubernetes is ripe to allow for integrating new technologies, like Artificial Intelligence (AI) and Machine Learning (ML). As an open-source platform, Kubernetes orchestrates containerized applications, ensuring they run efficiently and resiliently.

In my last blog post on feature extraction, I mentioned something called the bag-of-words (BoW) technique. I decided to write a little bit more on that, mostly just because I think it’s a funny label for something so technical.

Patch management software automatically applies updates to software, firmware, and other system components. Patching makes sure resources are up to date with the latest security and performance improvements to keep software protected and performing as expected.