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I saw three AI migrations fail. The technology worked. The teams had skill. But bad architectural choices ruined the projects. Problems appeared months later. Costs rose. Speed dropped.
Avoid these five mistakes to keep your AI project alive.
- Premature splitting Do not break AI tools into tiny services based on theory. This creates network lag. It makes debugging hard.
- Start with large groups.
- Split modules only when data shows a need.
- Poor schema control Updating a data field often breaks other modules. This stops independent growth.
- Treat schemas as contracts.
- Use clear versioning.
- Weak state management AI needs consistent data. If one module uses old data, your user gets bad results.
- Use a central feature store.
- Use versioned storage for model files.
- Ignoring partial failures One module fails and the whole system stops. This removes the benefit of modularity.
- Plan for failure.
- Use cached data or default settings as a backup.
- Missing performance targets One slow module ruins your total speed.
- Set Service Level Objectives (SLOs) for every module.
- Monitor these targets daily.
Modular AI helps you scale and innovate. It needs discipline. Focus on boundaries and contracts.
Source: https://dev.to/edith_heroux_aca4c9046ef5/5-critical-mistakes-to-avoid-when-implementing-modular-ai-integration-3k4g Optional learning community: https://t.me/GyaanSetuAi