Over the last few weeks I've been talking about the key differences between Amazon EKS and Cycle. If you happened to miss it and want to catch up before diving into this post you can check it out here: This post will round out the series by taking a look at how worker nodes are added to a cluster and the major differences between EKS and Cycle there.

Load balancers are some of the most important load-bearing (pun intended) components in cloud environments. They perform multiple critical tasks: network switching, packet inspection, and of course, routing. Most cloud-based load balancers focus on load balancing within a single zone, but what if you have resources spread across multiple zones?

Trying to find the best virtual machine on the market that gives you the flexibility of easy scalability and the promise of a secure network – and doesn’t cost an arm and a leg (and maybe another arm)? Azure VM is likely the best solution for you… assuming you can project costs correctly. However, Azure doesn’t make it easy with its different offerings and pricing models.

Enterprise generative artificial intelligence (GenAI) projects are gaining traction as organizations seek ways to stay competitive and deliver benefits for their customers. According to McKinsey, scaling these initiatives is challenging due to the required workflow changes. With AI adoption on the rise across industries, the need for robust monitoring and observability solutions has never been greater.

Robotic process automation (RPA) initiatives often falter because enterprise application leaders choose use cases that involve intricate business logic, extended workflows, and require significant orchestration or additional technologies to deliver business value. In essence, RPA is not well-suited for complex workflows, lengthy processes, and those requiring extensive coordination.

Foundation models, or large AI models, are the driving force behind the advancement of generative AI applications that cover an ever-growing list of use cases including chatbots, code completion, autonomous agents, image generation and more. However, when it comes to understanding observability metrics, current large language models (LLMs) are not optimal.

Hybrid work is very much here to stay. But in spite of that, many companies still haven’t come around to the idea, meaning the tools and systems offered aren’t always used to best effect. This leaves these businesses unprepared to embrace the hybrid world of work we now all find ourselves in. Take hybrid meetings for example – which combine in-person and remote participation, using digital tools to facilitate communication and collaboration across various locations.

Over the past six years at Megaport, I’ve witnessed a growing desire for change in the Financial Services (FS) industry, particularly regarding application and data mobility within organizations and with third-party partners. Initially, many in FS approached cloud adoption tentatively, waiting until it was fully matured and trusted across the industry. Today, however, the FS sector is one of the largest users of cloud services globally.

At Keysight Technologies, engineers are the core of the business. These engineers place high demand on their technology, to support the high demands of their work. If engineers are bogged down by technical issues, that has a direct impact on product development and business outlook – creating an imperative for IT at Keysight Technologies to provide a seamless digital workplace.

As Data Scientists and Engineers in a fast-paced corporate environment, our daily activities are filled with numerous challenges. One of the most daunting obstacles we face is the management of infrastructure and related costs. Each task or model we work on must be scheduled meticulously, cost-optimized, and equipped with the right CPU and memory specifications. On top of that, we must also supervise their dependencies.