Panel-ready MLOps track questions for freshers and experienced developers. Practice at Coding Cadre in Faridabad, or Online from Delhi NCR.
MLOps is the practice of deploying, monitoring, and iterating ML systems reliably like DevOps for models.
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CI runs tests on code and sometimes data validation.
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Docker freezes dependencies so the model runs the same in laptop, CI, and cloud.
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A store for versioned model artifacts and metadata.
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Without versions you cannot reproduce a metric or roll back a bad release.
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The distribution of model outputs changes over time.
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Send a small traffic slice to the new model and compare metrics.
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Batch scores many rows on a schedule
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So experiments are reproducible and audits can explain a model.
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Ground truth arrives late, so you monitor proxy metrics and delayed evaluation windows.
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A consistent place to serve features for training and inference.
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New model scores live traffic without affecting users.
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Storing versioned model artifacts with metadata and stage tags like Staging or Prod.
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Batch scores many rows on a schedule
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Training code unit tests, data schema checks, and a smoke train on a tiny sample.
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A small slice of traffic proves metrics before full rollout.
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Pin dependencies, log data versions, seed where possible, and record hardware.
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Access control, signed artifacts, and no pickle from untrusted sources.
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Same runtime in dev and prod, pinned system libs, and easier rollbacks.
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Latency SLOs, queue depth, and CPU/GPU utilization.
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