Maswali yanayounganishwa na mwongozo · Kati Kiwango

CI/CD ya Maswali ya Kujifunza Mashine

Angalia jinsi mabomba ya utoaji wa ML yanavyothibitisha data, kutoa mafunzo na kutathmini modeli, kutumia milango ya kukuza, na kupatanisha hali ya upelekaji.

Njia za mwongozo zinazohusianaCi Cd Kwa Kujifunza Mashine
Swali 1 ya 8

CI/CD ya kujifunza kwa mashine inaongeza nini zaidi ya programu ya kitamaduni ya CI/CD?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. CI/CD ya kujifunza kwa mashine inaongeza nini zaidi ya programu ya kitamaduni ya CI/CD?
    • Uendeshaji wa data na wasiwasi wa mfano, sio nambari tu
    • Hakuna kitu - ni sawa na programu CI/CD
    • Mkusanyiko wa haraka tu
    • Lugha mpya ya programu
  2. 'CT' katika muktadha wa bomba la ML kawaida husimamia nini?
    • Uhamisho wa Msimbo
    • Mafunzo ya Kuendelea
    • Upimaji wa Kontena
    • Telemetry ya kati
  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. Ni ipi kati ya hizi ni zana inayotumiwa sana kupanga mabomba ya ML?
    • Neno la Microsoft
    • VLC Media Player
    • WinRAR
    • Kubeflow Pipelines
  5. Kwa nini bomba la ML linaweza kujumuisha hatua ya uthibitishaji wa schema ya data?
    • Ili kupata data ya pembejeo iliyoharibika au iliyosogezwa kabla ya kupotosha mafunzo
    • Ili kufanya GPU ifanye kazi vizuri zaidi
    • Ili kubana mkusanyiko wa data
    • Ili kubadilisha safuwima nasibu
  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