MLOps (Machine Learning Operations) japplikaa prattiki simili għal DevOps għal ML — immuntar iċ-ċiklu ħajja ta' ML kollu (data, taħriġ, distribuzzjoni, monitoraġġ) b'mod affidabbli u fuq skala. Jittratta l-isfidi operazzjonali uniċi għad-distribuzzjoni u manutenzjoni ta' ML fil-produzzjoni.
Iċ-ċiklu ħajja ta' 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
