Repository Analysis

DataTalksClub/mlops-zoomcamp

Free MLOps course from DataTalks.Club

6.3 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of DataTalksClub/mlops-zoomcamp, a Jupyter Notebook project with 14,952 GitHub stars. SynthScan v2.0 examined 16,101 lines of code across 205 source files, recording 58 pattern matches distributed across 7 syntactic categories. The overall adjusted score of 6.3 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

6.3
Adjusted Score
6.3
Raw Score
100%
Time Factor
2026-06-10
Last Push
15.0K
Stars
Jupyter Notebook
Language
16.1K
Lines of Code
205
Files
58
Pattern Hits
2026-07-14
Scan Date
0.03
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 6MEDIUM 6LOW 46

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 58 distinct pattern matches across 7 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Cross-File Repetition6 hits · 30 pts
SeverityFileLineSnippetContext
HIGH…3-orchestration/prefect/3.3/orchestrate_pre_prefect.py0train a model with best hyperparams and write everything outSTRING
HIGH…horts/2023/03-orchestration/prefect/3.3/orchestrate.py0train a model with best hyperparams and write everything outSTRING
HIGH…horts/2023/03-orchestration/prefect/3.4/orchestrate.py0train a model with best hyperparams and write everything outSTRING
HIGH…horts/2023/03-orchestration/prefect/3.5/orchestrate.py0train a model with best hyperparams and write everything outSTRING
HIGH…ts/2023/03-orchestration/prefect/3.5/orchestrate_s3.py0train a model with best hyperparams and write everything outSTRING
HIGH…ts/2023/03-orchestration/prefect/3.6/orchestrate_s3.py0train a model with best hyperparams and write everything outSTRING
Unused Imports28 hits · 28 pts
SeverityFileLineSnippetContext
LOW04-deployment/web-service-mlflow/predict.py2CODE
LOW04-deployment/batch/score.py4CODE
LOW04-deployment/batch/score.py8CODE
LOW04-deployment/batch/score.py21CODE
LOW04-deployment/batch/score.py22CODE
LOW04-deployment/batch/score.py23CODE
LOW04-deployment/batch/score.py24CODE
LOWcohorts/2022/03-orchestration/code/model_training.py1CODE
LOWcohorts/2022/03-orchestration/code/model_training.py6CODE
LOWcohorts/2022/03-orchestration/code/model_training.py6CODE
LOWcohorts/2022/03-orchestration/code/model_training.py6CODE
LOWcohorts/2022/03-orchestration/code/orchestration.py5CODE
LOWcohorts/2022/03-orchestration/code/orchestration.py5CODE
LOWcohorts/2022/03-orchestration/code/orchestration.py5CODE
LOWcohorts/2022/03-orchestration/code/orchestration.py153CODE
LOWcohorts/2022/03-orchestration/code/orchestration.py154CODE
LOW…3-orchestration/prefect/3.3/orchestrate_pre_prefect.py11CODE
LOW…3-orchestration/prefect/3.3/orchestrate_pre_prefect.py11CODE
LOW05-monitoring/dummy_metrics_calculation.py7CODE
LOW05-monitoring/dummy_metrics_calculation.py8CODE
LOW05-monitoring/evidently_metrics_calculation.py5CODE
LOW05-monitoring/evidently_metrics_calculation.py6CODE
LOW05-monitoring/evidently_metrics_calculation.py8CODE
LOW…toring/post-evidently-0.7/dummy_metrics_calculation.py7CODE
LOW…toring/post-evidently-0.7/dummy_metrics_calculation.py8CODE
LOW…ng/post-evidently-0.7/evidently_metrics_calculation.py5CODE
LOW…ng/post-evidently-0.7/evidently_metrics_calculation.py6CODE
LOW…ng/post-evidently-0.7/evidently_metrics_calculation.py8CODE
Structural Annotation Overuse10 hits · 21 pts
SeverityFileLineSnippetContext
LOW03-orchestration/README.md23### Step 1: Choosing the ToolCOMMENT
LOW03-orchestration/README.md34### Step 2: Running the ToolCOMMENT
LOW03-orchestration/README.md39### Step 3: Orchestrating the WorkflowCOMMENT
LOW03-orchestration/README.md44### Step 4: Parametrizing the WorkflowCOMMENT
LOW03-orchestration/README.md50### Step 5: BackfillingCOMMENT
LOW03-orchestration/README.md54### Step 6: Deployment (optional)COMMENT
LOW01-intro/README.md37### Step 1: Download and install the Anaconda distribution of PythonCOMMENT
LOW01-intro/README.md43### Step 2: Update existing packagesCOMMENT
LOW01-intro/README.md49### Step 3: Install Docker and Docker ComposeCOMMENT
LOW01-intro/README.md79### Step 4: Run DockerCOMMENT
Modern AI Meta-Vocabulary3 hits · 9 pts
SeverityFileLineSnippetContext
MEDIUMREADME.md105### [Module 3: Orchestration & ML Pipelines](03-orchestration)COMMENT
MEDIUMcohorts/2022/03-orchestration/README.md7## 3.1 Negative engineering and workflow orchestrationCOMMENT
MEDIUMcohorts/2024/03-orchestration/README.md15## [3.6 Homework](../cohorts/2024/03-orchestration/homework.md).COMMENT
Self-Referential Comments3 hits · 9 pts
SeverityFileLineSnippetContext
MEDIUM…ts/2023/02-experiment-tracking/homework-wandb/train.py47 # Define the XGBoost Regressor Mode, train the model and perform predictionCOMMENT
MEDIUM…ts/2023/02-experiment-tracking/homework-wandb/sweep.py28 # Define the XGBoost Regressor Mode, train the model and perform predictionCOMMENT
MEDIUM.github/workflows/cd-deploy.yml25 # Define the infrastructureCOMMENT
Hyper-Verbose Identifiers7 hits · 4 pts
SeverityFileLineSnippetContext
LOW04-deployment/batch/score_backfill.py10def ride_duration_prediction_backfill():CODE
LOW05-monitoring/dummy_metrics_calculation.py34def calculate_dummy_metrics_postgresql(curr):STRING
LOW05-monitoring/evidently_metrics_calculation.py65def calculate_metrics_postgresql(curr, i):STRING
LOW05-monitoring/evidently_metrics_calculation.py87def batch_monitoring_backfill():STRING
LOW…toring/post-evidently-0.7/dummy_metrics_calculation.py34def calculate_dummy_metrics_postgresql(curr):STRING
LOW…ng/post-evidently-0.7/evidently_metrics_calculation.py69def calculate_metrics_postgresql(i):STRING
LOW…ng/post-evidently-0.7/evidently_metrics_calculation.py94def batch_monitoring_backfill():STRING
Over-Commented Block1 hit · 1 pts
SeverityFileLineSnippetContext
LOWcohorts/2022/03-orchestration/code/model_training.py61 return X_train, X_val, y_train, y_val, dvCOMMENT