Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

In part one of this series, we covered how you could limit deployments to Upsun only when a tag is pushed/created, focusing primarily on using GitHub and the GitHub Actions platform to accomplish this goal. But we’re a polyglot PaaS and strive to be agnostic in our users’ source code management terms of the service. With that in mind, let’s look at how we can accomplish the same goal using GitLab and your CI/CD system. Just like last time, there are some assumptions to consider.