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Stop chasing the ideal MLOps pipeline and start building for evolution. Your future self will thank you. I've seen teams spend months crafting what they believe is the ultimate MLOps setup, only to find it's a brittle monolith a year later. Think carefully about the choices you make today. One example? Over-reliance on custom scripting instead of embracing managed services. Short-term gains can turn into long-term maintenance nightmares. Another trap: neglecting proper data versioning and lineage tracking. When models start to drift, debugging becomes a herculean task. Prioritize flexibility, automation, and observability. Consider infrastructure-as-code, automated testing, and comprehensive monitoring from the outset. It's about building a pipeline that can adapt to changing requirements and scale efficiently. ⚙️
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