Since almost the beginning of programming, the idea of write-once and deploy everywhere, on all platforms, has been an unreachable ideal to minimize development costs for cross-platform applications, drive UI consistency and reduce security service area. In programming, the cross-platform languages Java and Python have topped developer utilization charts for decades.

PHP is one of the most popular programming languages on the web. It powers many widely used content management systems like WordPress and Drupal, and provides the backbone for modern server-side frameworks like Laravel and Symfony. Despite its popularity, PHP has a bit of a reputation for being slow and hard to maintain. It has gotten better in recent years, but there are two features that high-performance PHP applications will likely need: OPcache and PHP FastCGI Process Manager (PHP-FPM).

In Rancher 2.4, the latest release of Rancher Labs’ open source Kubernetes management platform, you can now manage K3s cluster upgrades from the Rancher UI. K3s is a lightweight Kubernetes distribution from Rancher that you can use to set up your development Kubernetes environment within minutes. It is great for production use cases and is built primarily for IOT and Edge devices. In Rancher 2.4, you can import K3s clusters and can manage the upgrades for it via Rancher itself.

Our very own regional director of Northern EMEA, Jeroen Overmaat, recently joined our partner, Magic Sandbox BV (MSB), for the inaugural episode of Magic Devcast, their new technology podcast. Magic Devcast brings together technology industry personalities and influencers from around the world to discuss how they tackle the ever-changing landscape, how to approach remote work, learning and much more.

In part one of this series, I introduced you to Kubeflow, a machine learning platform for teams that need to build machine learning pipelines. In this section, we will learn how to take an existing machine learning project and turn it into a Kubeflow machine learning pipeline, which in turn can be deployed onto Kubernetes. As you are going through this exercise, think about how you can convert your existing machine learning projects into a Kubeflow one.

The public cloud offers great scalability and flexibility for customers and is a model where service providers make many decisions on their behalf. For example, in cloud service providers like Google Cloud Platform (GCP), Amazon Web Service (AWS) or Microsoft Azure, a cloud load balancer is spun up on demand. The load balancer gets an IP address automatically and your application is ready to be served.