MLOps — ML Operations — DevOps prinsiplarini machine learning ga tatbiq etish. Muammo: data science notebook da yaxshi ishlaydigan model production da boshqacha ishlaydi. "Deployment gap" — bu sohadagi eng katta muammo. 87% ML loyihalari production ga yetib bormaydi.
MLOps stack: Data versioning (DVC), Experiment tracking (MLflow, Weights & Biases), Model registry, CI/CD pipeline (GitHub Actions), Model serving (FastAPI, TorchServe, BentoML), Monitoring (data drift, model performance degradation). Feature store — real-vaqt va batch feature larni boshqaradi. A/B testing production da yangi model ni eski bilan taqqoslash imkonini beradi.
Bu mavzu bo'yicha kurslarimiz