Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

From its initial appearance in the dev-tools space, GenAI has had an outsized impact on how developers approach day-to-day tasks (just ask any developer about when they first started using GitHub’s copilot). While any risks are still being evaluated—like potential for introducing anti-patterns or inadvertently running afoul of compliance requirements, many engineering teams have successfully implemented GenAI with measurable gains in collaboration and productivity.

As businesses increasingly move their workloads to the cloud, managing cloud costs has become a critical concern. At Tidal.Cloud, with the roll-out of new features such as Tidal Accelerator’s Business Analytics, we recently realized that our AWS costs were steadily increasing, and we needed to take action to optimize our cloud spending.

Tidal Accelerator has recently made its powerful Business Analytics feature generally available across all existing and new workspaces, as discussed in our previous article. This game-changing capability empowers organizations to make data-driven decisions throughout their migration and modernization journeys. In this article, we’ll dive deeper into four key areas where Tidal Accelerator’s advanced analytics provide invaluable insights.