AWS Elastic Beanstalk streamlines the deployment and management of applications in the cloud, offering developers an easy way to deploy services without worrying about the underlying infrastructure. However, while it simplifies many aspects of cloud deployment, it also presents challenges, including limited customization and potential vendor lock-in, which might not suit all project needs or scalability requirements.

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.