Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

CI / CD fun Idanwo Ẹkọ Ẹrọ

Check how ML delivery pipelines validate data, train and evaluate models, apply promotion gates, and reconcile deployment state.

Jẹmọ itọsọna awọn ọnaCi CD Fun Ẹkọ ẹrọ
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Kini CI/CD fun ẹkọ ẹrọ ṣe afikun ju sọfitiwia ibile CI/CD?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini CI/CD fun ẹkọ ẹrọ ṣe afikun ju sọfitiwia ibile CI/CD?
    • Adaṣiṣẹ ti data ati awọn ifiyesi awoṣe, kii ṣe koodu nikan
    • Ko si ohun - o jẹ aami si software CI/CD
    • Nikan yiyara akopo
    • Ede siseto tuntun
  2. Kini 'CT' ni aaye opo gigun ti epo ML nigbagbogbo duro fun?
    • Gbigbe koodu
    • Ikẹkọ Ilọsiwaju
    • Apoti Igbeyewo
    • Central Telemetry
  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. Ewo ninu iwọnyi jẹ irinṣẹ ti o wọpọ julọ lati ṣe orchestrate awọn paipu ML?
    • Microsoft Ọrọ
    • VLC Media Player
    • WinRAR
    • Kubeflow Pipelines
  5. Kini idi ti opo gigun ti epo ML pẹlu igbesẹ afọwọsi ero data kan?
    • Lati yẹ aiṣedeede tabi data igbewọle ti lọ kuro ṣaaju ki o ba ikẹkọ jẹ
    • Lati jẹ ki GPU ṣiṣẹ kula
    • Lati compress awọn dataset
    • Lati tunrukọ awọn ọwọn laileto
  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