Kedis ku xidhan hagaha · Dhexdhexaad Heerka

CI/CD ee Kediska Barashada Mashiinka

Hubi sida dhuumaha gaarsiinta ML u ansixiyaan xogta, u tababaraan una qiimeeyaan moodooyinka, u dabaqaan albaabbada dallacsiinta, iyo dib u heshiisiinta gobolka.

Wadooyinka haga ee la xidhiidhaCi Cd ee Barashada Mashiinka
Su'aal 1 ee 8

Maxay CI/CD ee barashada mishiinka ku kordhinaysaa wax ka baxsan software-dhaqameedka CI/CD?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Maxay CI/CD ee barashada mishiinka ku kordhinaysaa wax ka baxsan software-dhaqameedka CI/CD?
    • Automation of data iyo model walaac, ma aha oo kaliya code
    • Waxba - waxay la mid tahay software CI/CD
    • Kaliya isku-dubarid degdeg ah
    • Luuqad barnaamijeed cusub
  2. Waa maxay 'CT' ee macnaha guud ee dhuumaha ML caadi ahaan u taagan yahay?
    • Wareejinta Koodhka
    • Tababar joogto ah
    • Tijaabada weelka
    • Telemetry dhexe
  3. What is a common evaluation gate before promoting a candidate ML model?
    • Promote it whenever training completes
    • Compare only model file sizes
    • Compare it with the production baseline on agreed metrics and promote only if policy thresholds pass
    • Skip evaluation if the code tests pass
  4. Midkee ka mid ah qalabkan sida caadiga ah loo isticmaalo in lagu abaabulo dhuumaha ML?
    • Microsoft Eray
    • VLC Media Player
    • WinRAR
    • Dhuumaha Kubeflow
  5. Waa maxay sababta laga yaabo in dhuumaha ML ay ku jiraan tillaabada ansixinta qorshaha xogta?
    • Si aad u qabsato xogta galinta ee khaldan ama leexleexatay ka hor inta aanay xumaanin tababarka
    • Si GPU-gu u shaqeeyo qaboojiye
    • Si loo cadaadiyo kaydka xogta
    • Si aad u magacawdo tiirarka si aan kala sooc lahayn
  6. In Flux GitOps, what does reconciliation aim to do?
    • Retrain every model whenever code is committed
    • Make the deployed environment match the declared desired state
    • Automatically change a model’s evaluation labels
    • Replace version-controlled configuration with manual edits
  7. What is a useful model-artifact versioning practice?
    • Ignore training inputs after a model is deployed
    • Replace validation with a deployment timestamp
    • Version model artifacts with source data, code, and evaluation metadata
    • Use a schema check as a guarantee of model quality
  8. What is a typical trigger for continuous training in an ML pipeline?
    • Every inference request must retrain the model
    • A model file being downloaded by a user
    • A CPU core reaching a particular temperature
    • A scheduled run, new approved data, or a monitoring signal such as drift