Practical thinking on technology, training and careers
Notes from our trainers and consultants — no recycled listicles, just what we've actually learned running programs and engagements.
Cloud Certification
5 Cloud Certifications Worth Pursuing in 2026
With AWS, Azure and Google Cloud all refreshing their certification tracks in the past year, it's worth being deliberate about which one actually moves your career forward. AWS Solutions Architect Associate remains the broadest-value credential for generalist cloud engineers, while Azure Administrator is the stronger pick if your organization is already a Microsoft shop. For teams leaning into data and ML workloads, Google Cloud's Professional Data Engineer credential has seen the fastest hiring-demand growth of the three. Rather than collecting certifications for their own sake, match the credential to the cloud your target employer or client actually runs on — a single well-chosen certification backed by real hands-on project work outperforms three unused ones on a resume.
How to Structure a Corporate Upskilling Program That Actually Sticks
Most corporate training fails not because the content is wrong, but because it's scheduled and measured wrong. The programs that hold up six months later share three traits: they're scoped to a specific business outcome (a migration, an audit, a new stack) rather than generic "upskilling"; they include a pre-training baseline assessment so progress is measurable rather than assumed; and they build in post-training office hours, because most real learning happens in the weeks after the last live session when people hit their first real problem. If you're scoping a program for your team, start with the outcome you want six months from now, and work backwards into the curriculum — not the other way around.
Before greenlighting another generative AI pilot, most mid-size enterprises would benefit from answering four questions honestly: Is there a specific, measurable task this replaces or accelerates — not a vague productivity hope? Who owns the output when the model gets something wrong? What internal data, if any, does this system need access to, and has that been reviewed by security? And is there a plan to monitor the system after launch, or will it quietly degrade unnoticed? Teams that answer these upfront ship AI projects that survive contact with production; teams that skip straight to model selection tend to end up with an impressive demo and no lasting deployment.
