Some of the costs involved with cloud computing are obvious — the initial investment in technology, and professional services to implement a solution. There are also the consumption costs which should be managed and governed centrally. If some applications are staying on-premises, organizations should account for managing both. And then there’s the expense and time it requires to train, re-skill or up skill anyone involved in a cloud strategy and implementation.
“Training staff not only takes time, but it also takes those administrators and developers away from their regular responsibilities” Korolis says. “It takes time to develop training plans, and it takes time to study for certifications as well, no matter what solution you’re deploying.”
Part of the challenge comes from the need to find talent with a background in AI, he adds. This skill gap is an industry-wide challenge — and it will require not just up skill and re-skil, but a whole new cohort of job applicants to really fill the needs. That said, platform and technology certifications that test applicants on objective criteria are proving to be useful, Korolis says.
For instance, there are certifications like Azure AI engineer, among others. These certifications can often give engineers a head start on studying for other certifications, since some of the same foundational principles apply to other cloud workloads.