Managing application updates in production and ensuring the reliability of software releases in Kubernetes environments can be challenging. Small changes can sometimes lead to unforeseen issues in production. These unexpected problems, combined with the lack of scalability and the high costs associated with managing complex solutions, can be daunting.

This is a guest post authored by Nathan Skrzypczak, R&D Engineer at Cisco. Calico VPP, the latest addition to Calico’s suite of pluggable data planes, revolutionizes Kubernetes networking by enabling transparent user-space packet processing. With features such as service load balancing, encapsulation, policy enforcement, and encryption, Calico VPP brings the performance, flexibility, and observability of VPP to Kubernetes networking.

🚨 To read the full findings from this research, visit The Machine Learning State of Play 2024 white paper. Are the challenges of deploying machine learning (ML) overshadowing its true potential in the modern workplace? Through our recent white paper , we spoke to 500+ developers who have experience working with ML systems to gain an understanding of the pain points faced by developers when using ML solutions.

Last week in Paris, at KubeCon EU 2024, platform engineering was the talk of the event. The topic had a full-day co-located event and also a dedicated track during KubeCon itself. Here’s what I’ve learned from sitting in keynote rooms and then standing at the Spot and NetApp booth till my knees hurt. (Spoiler: It was well worth it.)

AI has had a massive impact on every part of our lives, but mainly on how we consume large data sets easily. The observability world is based on collecting enormous amounts of data and consuming it by observing dashboards built on monitoring tools. Most of the o11y tasks like writing complex queries, creating dashboards & defining alerts, have been done much in the same way for the last decade & AI models are well-positioned to disrupt this modus operandi.

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.

Ensuring efficient deployment and management of containerized applications is critical to development teams across all possible industries in today’s cloud-native world. A standout service for many has been AWS Elastic Container Service (ECS), which has proven to be scalable and easy to use while integrated into one of the largest cloud ecosystems out there. However, one size does not fit all.

If there's one thing almost every developer has done in their life, it's deploy an application to Heroku. From their launch in 2007, Heroku dominated PaaS and caught the attention of Salesforce who acquired them in 2010. They had the first managed Postgres service in 2011, an easy to use CLI, and, last but not least, it was free to get started which led to a big uptick in initial popularity. Every side project from here to the moon went to Heroku.

StackState had a blast at this year's KubeCon + CloudNativeCon gathering in Paris! The discussions were in-depth, covering a wide array of topics and lasting much longer than in the past. This year, attendees seemed to have a considerably deeper understanding of the cloud-native ecosystem, probably attributed to its rapid growth. We also noticed a pretty dramatic evolutionary shift in the vendors at the expo hall, who were showcasing some truly progressive specialized solutions.