Modern Microservices for Enterprise AI: Patterns, Pitfalls, and Practices
summary
Advocates splitting systems into 7–10 mini‑services rather than hundreds, citing an Oracle auth system of ~30 services as a large real example, and warns that one database per service creates consistency and security headaches. Recommends a converged database approach (example: Oracle AI Database) or at least multi‑model DBs, REST for public APIs and event‑driven internals with transactional event queues to guarantee exactly‑once semantics, plus workflow/transaction manager (microtx) and an open‑source AI optimizer for rapid model and embedding experiments.
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chapters (9)
- 0:00Introduction to AI microservices
- 5:04Microservices and domains
- 8:38Converged database architecture
- 13:28Database multi-model strategy
- 15:55Event-driven communication
- 24:15Transactions and workflows
- 30:20Database-level security
- 35:47Design patterns and platform
- 38:22Rapid experimentation and review
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