mlops-validation

Name: mlops-validation
Author: MLOps-Courses

19stars3forks

How to install mlops-validation?

mlops-courses/mlops-coding-skills/mlops-validation

$npx -y skills add MLOps-Courses/mlops-coding-skills --skill mlops-validation

Installs into the current project.

Summary

Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.

Files · 1

View on GitHub

SKILL.md

1	# MLOps Validation
2
3	## Goal
4
5	To ensure software quality, reliability, and security through automated validation layers. This skill enforces Strict Typing (`ty`), Unified Linting (`ruff`), Comprehensive Testing (`pytest`), and Structured Logging (`loguru`).
6
7	## Prerequisites
8
9	- Language: Python
10	- Manager: `uv`
11	- Context: Ensuring code quality before merge/deploy.
12
13	## Instructions
14
15	### 1. Static Analysis (Typing & Linting)
16
17	Catch errors before they run.
18
19	1. Typing:
20	- Tool: `ty` (Astral type checker; pre-1.0, pin a compatible range). The mandated checker — do not use `mypy`.
21	- Rule: No `Any` (unless absolutely necessary). Fully typed function signatures.
22	- DataFrames: Use `pandera` schemas to validate DataFrame structures/types.
23	- Classes: Use `pydantic` for data modeling and runtime validation.
24	2. Linting & Formatting:
25	- Tool: `ruff` (replaces black, isort, pylint, flake8).
26	- Rule: Zero tolerance for linter errors. Use `noqa` sparingly and with justification.
27	- Config: Centralize in `pyproject.toml`.
28
29	### 2. Testing Strategy
30
31	Verify behavior and prevent regressions.
32
33	1. Tool: `pytest`.
34	2. Structure: Mirror `src/` in `tests/`.
35
36	```text
37	src/pkg/mod.py -> tests/test_mod.py
38	```
39
40	3. Fixtures: Use `tests/conftest.py` for shared setup (mock data, temp paths).
41	4. Coverage: Aim for high coverage (>80%) on core business logic. Use `pytest-cov`.
42	5. Pattern: Use Given-When-Then in comments.
43
44	```python
45	def test_pipeline_execution(input_data):
46	# Given: Valid input data
47	# When: The pipeline processes the data
48	# Then: The output content matches expectations
49	```
50
51	### 3. Structured Logging
52
53	Enable observability and debugging.
54
55	1. Tool: `loguru` (replacing stdlib `logging`).
56	2. Format: Use structured logging (JSON) in production for queryability.
57	3. Levels:
58	- `DEBUG`: Low-level tracing (payloads, internal state).
59	- `INFO`: Key business events (Job started, Model saved).
60	- `ERROR`: Actionable failures (with stack traces).
61	4. Context: Include context (Job ID, Model Version) in logs.
62
63	### 4. Security
64
65	Protect the supply chain and runtime.
66
67	1. Code Scanning: Enable Ruff `S` (flake8-bandit) rules to detect unsafe patterns (e.g., `eval`, `yaml.load`) — this replaces standalone `bandit`.
68	1. Dependencies: Run `pip-audit` (and/or `GitHub Dependabot`) to patch vulnerable packages.
69	1. Secret Scanning: Run `gitleaks` to keep credentials out of the code and git history.
70	1. Secrets: NEVER log secrets. Sanitize outputs.
71
72	## Self-Correction Checklist
73
74	- [ ] Type Safety: Does `ty` pass without errors?
75	- [ ] Lint Cleanliness: Does `ruff check` pass?
76	- [ ] Test Discovery: Does `pytest` successfully find modules in `src/`?
77	- [ ] Log Format: Are production logs serializing to JSON?
78	- [ ] Security: Do Ruff `S` rules, `pip-audit`, and `gitleaks` pass?

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mlops-validation

19stars3forks

View on GitHub

How to install mlops-validation?

mlops-courses/mlops-coding-skills/mlops-validation

$npx -y skills add MLOps-Courses/mlops-coding-skills --skill mlops-validation

Installs into the current project.

Summary

Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.

Files · 1

View on GitHub

SKILL.md

1	# MLOps Validation
2
3	## Goal
4
5	To ensure software quality, reliability, and security through automated validation layers. This skill enforces Strict Typing (`ty`), Unified Linting (`ruff`), Comprehensive Testing (`pytest`), and Structured Logging (`loguru`).
6
7	## Prerequisites
8
9	- Language: Python
10	- Manager: `uv`
11	- Context: Ensuring code quality before merge/deploy.
12
13	## Instructions
14
15	### 1. Static Analysis (Typing & Linting)
16
17	Catch errors before they run.
18
19	1. Typing:
20	- Tool: `ty` (Astral type checker; pre-1.0, pin a compatible range). The mandated checker — do not use `mypy`.
21	- Rule: No `Any` (unless absolutely necessary). Fully typed function signatures.
22	- DataFrames: Use `pandera` schemas to validate DataFrame structures/types.
23	- Classes: Use `pydantic` for data modeling and runtime validation.
24	2. Linting & Formatting:
25	- Tool: `ruff` (replaces black, isort, pylint, flake8).
26	- Rule: Zero tolerance for linter errors. Use `noqa` sparingly and with justification.
27	- Config: Centralize in `pyproject.toml`.
28
29	### 2. Testing Strategy
30
31	Verify behavior and prevent regressions.
32
33	1. Tool: `pytest`.
34	2. Structure: Mirror `src/` in `tests/`.
35
36	```text
37	src/pkg/mod.py -> tests/test_mod.py
38	```
39
40	3. Fixtures: Use `tests/conftest.py` for shared setup (mock data, temp paths).
41	4. Coverage: Aim for high coverage (>80%) on core business logic. Use `pytest-cov`.
42	5. Pattern: Use Given-When-Then in comments.
43
44	```python
45	def test_pipeline_execution(input_data):
46	# Given: Valid input data
47	# When: The pipeline processes the data
48	# Then: The output content matches expectations
49	```
50
51	### 3. Structured Logging
52
53	Enable observability and debugging.
54
55	1. Tool: `loguru` (replacing stdlib `logging`).
56	2. Format: Use structured logging (JSON) in production for queryability.
57	3. Levels:
58	- `DEBUG`: Low-level tracing (payloads, internal state).
59	- `INFO`: Key business events (Job started, Model saved).
60	- `ERROR`: Actionable failures (with stack traces).
61	4. Context: Include context (Job ID, Model Version) in logs.
62
63	### 4. Security
64
65	Protect the supply chain and runtime.
66
67	1. Code Scanning: Enable Ruff `S` (flake8-bandit) rules to detect unsafe patterns (e.g., `eval`, `yaml.load`) — this replaces standalone `bandit`.
68	1. Dependencies: Run `pip-audit` (and/or `GitHub Dependabot`) to patch vulnerable packages.
69	1. Secret Scanning: Run `gitleaks` to keep credentials out of the code and git history.
70	1. Secrets: NEVER log secrets. Sanitize outputs.
71
72	## Self-Correction Checklist
73
74	- [ ] Type Safety: Does `ty` pass without errors?
75	- [ ] Lint Cleanliness: Does `ruff check` pass?
76	- [ ] Test Discovery: Does `pytest` successfully find modules in `src/`?
77	- [ ] Log Format: Are production logs serializing to JSON?
78	- [ ] Security: Do Ruff `S` rules, `pip-audit`, and `gitleaks` pass?

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