MLOps (Machine Learning Operations) cuireann sé cleachtais cosúil le DevOps i bhfeidhm ar ML — an timthriall ML iomlán (sonraí, traenáil, cur i bhfeidhm, monatóireacht) a bhainistiú go iontaofa agus ar scála. Tugann sé aghaidh ar dhúshláin oibríochtúla uathúla maidir le cur i bhfeidhm ML agus a bhainistiú i dtáirgealadh.
An timthriall ML
ML projects involve a full lifecycle (not just training a model):
1. DATA → collect, clean, label, version data (data is foundational)
2. TRAINING → develop, train, and evaluate models (experimentation, tuning)
3. DEPLOYMENT → put the model into production (serving predictions/inference)
4. MONITORING → track performance in production; detect issues
5. MAINTENANCE → retrain/update models as data and performance change
→ an ongoing cycle, not a one-time effort
