Leadership brief · one page

Cloud Modernization Patterns

Aging applications get more expensive to run, harder to secure, and riskier to change every year they sit. This training teaches teams to modernize them in small, reversible steps instead of one all-at-once rewrite.

Self-paced · 6 modules, 22 lessons · about 7 hours

Chapter 1 in the Meridian sequence · 9 live so far — the sequence follows one fictional utility through the same modernization, so the examples build on each other. Each training stands on its own; the order is the recommended path, not a prerequisite. The story continues in Zero Trust Implementation.

What this training covers

Six modules, in the order the work actually happens: sizing up a legacy estate and deciding what to do with each application; choosing where each workload should run; moving the data without losing any of it; making what lands production-ready; using AI command-line tools for the repetitive work with people reviewing the results; and keeping the estate modern after the project ends.

Why it matters

Most modernization damage comes from one expensive pattern: a single big-bang rewrite that ships late, corrupts data on the way over, and lands on infrastructure the team cannot run. Every module here is arranged to prevent that, by turning one bet-the-schedule project into a sequence of small, reversible moves that each prove themselves before the next one starts.

What changes in practice

Tags name what each shift affects most: calendar time, cost, or an audit finding avoided.

1. 1

   Assess before you build

   Cost

   Why it matters: each application gets a written call — modernize, adopt, extend, or retire — so nobody spends a year rebuilding something that should have been switched off · Module 1

2. 2

   Where each workload runs becomes a deliberate decision

   Cost

   Why it matters: teams work down a platform ladder and stop at the most managed option that can host the workload, so operations cost tracks what the workload actually needs · Module 2

3. 3

   Data moves on a recorded cut-point and an independent count check

   Cost

   Why it matters: every object's counts are verified against the agreed data contract before the switch, and cutover happens only after the check passes · Module 3

4. 4

   Production readiness is defined before go-live

   Audit finding avoided

   Why it matters: hardening, testing, promotion, and rollback are settled before the first user arrives rather than after the first incident · Module 4

5. 5

   AI does the repetitive migration work, human review sets the pace

   Cost

   Why it matters: migration moves at agent speed while every line that ships still has a named human owner · Module 5

6. 6

   Compliance evidence is produced by the pipeline (evidence as code)

   Calendar time

   Why it matters: the accreditation calendar, usually the real schedule constraint, starts shrinking once assessors trust what the pipeline produces · Module 6

Where the effort goes

Most of it lands in three places: one platform-selection discipline used across teams rather than per-team choices, the migration and reconciliation work itself, and an ownership registry so nothing ships without a named owner. The slowest part is not technical — assessors need to watch pipeline-produced evidence hold up once before they accept it the second time.

How you'll know it worked

• Applications behave the same after migration as before, proven before cutover — no post-migration incident spike, no emergency rollback cost
• The second compliance cycle draws its evidence from the pipeline instead of a package assembled by hand — measure the assessor review time against your own first cycle to see what that bought
• Zero orphaned applications: everything running has an owner and a retirement path