Dataset & Setup

MLOps — Pipelines & CI/CD — Student Lab

Offline lab: build a tiny reproducible ML pipeline and add CI-style checks.

0 — Config + pipeline entrypoint

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Solution

2

0 — Config + pipeline entrypoint

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Solution

3

1 — Data generation + schema checks

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Solution

4

2 — Logistic regression (NumPy)

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Solution

5

3 — Evaluation + quality gate

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Solution

6

4 — Artifacts (model + metrics)

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Solution

7

5 — Implement `run_pipeline` + CI-style checks

1

5 — Implement `run_pipeline` + CI-style checks

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Solution

8

6 — CI simulation: determinism + regression gate

2

6 — CI simulation: determinism + regression gate

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Solution

MLOps — Pipelines & CI/CD

MLOps — Pipelines & CI/CD

Progress — 0/8 tasks

Asked At

MLOps — Pipelines & CI/CD — Student Lab

0 — Config + pipeline entrypoint

0 — Config + pipeline entrypoint

1 — Data generation + schema checks

2 — Logistic regression (NumPy)

3 — Evaluation + quality gate

4 — Artifacts (model + metrics)

5 — Implement `run_pipeline` + CI-style checks

6 — CI simulation: determinism + regression gate

MLOps — Pipelines & CI/CD

MLOps — Pipelines & CI/CD

Progress — 0/8 tasks

Asked At

MLOps — Pipelines & CI/CD — Student Lab

0 — Config + pipeline entrypoint

0 — Config + pipeline entrypoint

1 — Data generation + schema checks

2 — Logistic regression (NumPy)

3 — Evaluation + quality gate

4 — Artifacts (model + metrics)

5 — Implement run_pipeline + CI-style checks

6 — CI simulation: determinism + regression gate

5 — Implement `run_pipeline` + CI-style checks