Cloudops, layered security, and a well-trained staff should all be on your radar for next year. Credit: Tumisu Cloud budgets have expanded in the last two years. Although most see the pandemic as the cause, the reality is that IT dollars have shifted to the cloud for pragmatic reasons as well, such as shutting down aging data centers and upgrading security. The mantra thus far has been “migrate, migrate, migrate,” lifting sets of applications and data from the traditional systems and plunking them down on one or more public clouds. Workload and data migration will continue to be where the money is being spent in the world of cloud computing. We’re now seeing more strategic tasks being introduced as well. Here are three things to consider adding to your 2022 to-do list. Focus more on cloudops needs, including AIops capabilities. Some enterprises have indeed built their cloud systems from the ground up with ops in mind, but for most enterprises it’s an afterthought to be dealt with at the end of the migration or development processes of net-new cloud systems. You need to do two things: First, figure out how ops is supposed to work in detail. This means writing ops playbooks or whatever planning approaches you use. Second, get the tools in place for cloudops: AIops, security, governance, network operations, etc. This is typically a mix of some old and many new tools. Consider layered security for your cloud-based systems. I’m seeing that most public cloud systems stood up today leverage minimum viable security (MVS). In other words, companies hope to get away with security systems that will meet the minimum-security requirements of their cloud-based applications and data. MVS is not a bad thing, but other security layers should be considered as well. Security managers come to mind, especially for more complex architectures such as multicloud. These can sit above your different MVS technologies and make identity management, encryption, authentication systems, etc. easier to operate and thus more secure. Think of it as a master control center for all of cloud and sometimes non-cloud security. Formally and consistently augment the skills of your team. If you think that cloud training within your company is one and done, you’re sorely mistaken. During the pandemic we learned to train on the fly using on-demand resources, cloud provider certification, and even structured on-the-job training where individuals could be mentored directly over Zoom. However, now that many of those skills have been obtained, some companies think training is over, at least as a larger strategic effort. Now is the time to figure out your approach to cloud-skills training moving forward. Which platform and provider will be strategic partners? Miss this, and you’ll find that staff won’t feel like an investment is being made in their careers. You’ll also learn that skills become dated, and costly mistakes start occurring. Training should be ongoing and ever-changing around the needs of the cloud teams. None of what I’ve said here should be new. The idea is to get these concepts funded, or strive for continuous funding. We’ve come this far. Let’s keep up the momentum. Related content analysis Strategies to navigate the pitfalls of cloud costs Cloud providers waste a lot of their customers’ cloud dollars, but enterprises can take action. By David Linthicum Nov 15, 2024 6 mins Cloud Architecture Cloud Management Cloud Computing analysis Understanding Hyperlight, Microsoft’s minimal VM manager Microsoft is making its Rust-based, functions-focused VM tool available on Azure at last, ready to help event-driven applications at scale. By Simon Bisson Nov 14, 2024 8 mins Microsoft Azure Rust Serverless Computing how-to Docker tutorial: Get started with Docker volumes Learn the ins, outs, and limits of Docker's native technology for integrating containers with local file systems. By Serdar Yegulalp Nov 13, 2024 8 mins Devops Cloud Computing Software Development news Red Hat OpenShift AI unveils model registry, data drift detection Cloud-based AI and machine learning platform also adds support for Nvidia NIM, AMD GPUs, the vLLM runtime for KServe, KServe Modelcars, and LoRA fine-tuning. By Paul Krill Nov 12, 2024 3 mins Generative AI PaaS Artificial Intelligence Resources Videos