Guide-yakabatana mibvunzo · Pakati Nhanho

CI/CD yeMibvunzo Yekudzidza Muchina

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

Nzira dzekutungamira dzinoenderanaCi Cd Yekudzidza Muchina
Mubvunzo 1 ye 8

Ko CI/CD yekudzidza yemuchina inowedzera chii kupfuura yechinyakare software CI/CD?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ko CI/CD yekudzidza yemuchina inowedzera chii kupfuura yechinyakare software CI/CD?
    • Automation yedata uye modhi zvinonetsa, kwete kodhi chete
    • Hapana - yakafanana nesoftware CI/CD
    • Chete nekukurumidza kuunganidza
    • Mutauro mutsva wepurogiramu
  2. Ko iyo 'CT' iri muML pombi mamiriro anowanzomirira chii?
    • Code Transfer
    • Kuramba Kudzidzisa
    • Container Testing
    • 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. Ndechipi cheichi chishandiso chinowanzoshandiswa kuronga ML mapaipi?
    • Microsoft Shoko
    • VLC Media Player
    • WinRAR
    • Kubeflow Pipelines
  5. Nei pombi yeML ichisanganisira danho rekusimbisa schema data?
    • Kubata zvisina kurongeka kana kudhonzwa data rekuisa risati rakanganisa kudzidziswa
    • Kuita kuti GPU iite inotonhorera
    • Kumanikidza dataset
    • Kutumidzazve makoramu zvisina tsarukano
  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