Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
This report summarizes scanner evidence for DataTalksClub/mlops-zoomcamp, a Jupyter Notebook project with 15,394 GitHub stars. SynthScan examined 19,208 lines of code across 267 source files, recording 49 pattern matches distributed across 6 syntactic categories. The legacy adjusted pattern signal is 4.4 (Low pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 15 finding(s).
Are imports declared and package/API names verified? 28 finding(s).
Are wrappers, guards, and repeated structures necessary? 13 finding(s).
Which changes need focused human review? 49 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 49 distinct pattern matches across 6 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. 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 configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …3-orchestration/prefect/3.3/orchestrate_pre_prefect.py | 0 | train a model with best hyperparams and write everything out | STRING |
| HIGH | …horts/2023/03-orchestration/prefect/3.3/orchestrate.py | 0 | train a model with best hyperparams and write everything out | STRING |
| HIGH | …horts/2023/03-orchestration/prefect/3.4/orchestrate.py | 0 | train a model with best hyperparams and write everything out | STRING |
| HIGH | …horts/2023/03-orchestration/prefect/3.5/orchestrate.py | 0 | train a model with best hyperparams and write everything out | STRING |
| HIGH | …ts/2023/03-orchestration/prefect/3.5/orchestrate_s3.py | 0 | train a model with best hyperparams and write everything out | STRING |
| HIGH | …ts/2023/03-orchestration/prefect/3.6/orchestrate_s3.py | 0 | train a model with best hyperparams and write everything out | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | 04-deployment/web-service-mlflow/predict.py | 2 | CODE | |
| LOW | 04-deployment/batch/score.py | 4 | CODE | |
| LOW | 04-deployment/batch/score.py | 8 | CODE | |
| LOW | 04-deployment/batch/score.py | 21 | CODE | |
| LOW | 04-deployment/batch/score.py | 22 | CODE | |
| LOW | 04-deployment/batch/score.py | 23 | CODE | |
| LOW | 04-deployment/batch/score.py | 24 | CODE | |
| LOW | cohorts/2022/03-orchestration/code/model_training.py | 1 | CODE | |
| LOW | cohorts/2022/03-orchestration/code/model_training.py | 6 | CODE | |
| LOW | cohorts/2022/03-orchestration/code/model_training.py | 6 | CODE | |
| LOW | cohorts/2022/03-orchestration/code/model_training.py | 6 | CODE | |
| LOW | cohorts/2022/03-orchestration/code/orchestration.py | 5 | CODE | |
| LOW | cohorts/2022/03-orchestration/code/orchestration.py | 5 | CODE | |
| LOW | cohorts/2022/03-orchestration/code/orchestration.py | 5 | CODE | |
| LOW | cohorts/2022/03-orchestration/code/orchestration.py | 153 | CODE | |
| LOW | cohorts/2022/03-orchestration/code/orchestration.py | 154 | CODE | |
| LOW | …3-orchestration/prefect/3.3/orchestrate_pre_prefect.py | 11 | CODE | |
| LOW | …3-orchestration/prefect/3.3/orchestrate_pre_prefect.py | 11 | CODE | |
| LOW | 05-monitoring/dummy_metrics_calculation.py | 7 | CODE | |
| LOW | 05-monitoring/dummy_metrics_calculation.py | 8 | CODE | |
| LOW | 05-monitoring/evidently_metrics_calculation.py | 5 | CODE | |
| LOW | 05-monitoring/evidently_metrics_calculation.py | 6 | CODE | |
| LOW | 05-monitoring/evidently_metrics_calculation.py | 8 | CODE | |
| LOW | …toring/post-evidently-0.7/dummy_metrics_calculation.py | 7 | CODE | |
| LOW | …toring/post-evidently-0.7/dummy_metrics_calculation.py | 8 | CODE | |
| LOW | …ng/post-evidently-0.7/evidently_metrics_calculation.py | 5 | CODE | |
| LOW | …ng/post-evidently-0.7/evidently_metrics_calculation.py | 6 | CODE | |
| LOW | …ng/post-evidently-0.7/evidently_metrics_calculation.py | 8 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | README.md | 105 | ### [Module 3: Orchestration & ML Pipelines](03-orchestration) | COMMENT |
| MEDIUM | 03-orchestration/01-ml-pipelines.md | 30 | ## Pipeline versus orchestration | COMMENT |
| MEDIUM | cohorts/2022/03-orchestration/README.md | 7 | ## 3.1 Negative engineering and workflow orchestration | COMMENT |
| MEDIUM | cohorts/2024/03-orchestration/README.md | 15 | ## [3.6 Homework](../cohorts/2024/03-orchestration/homework.md). | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | …ts/2023/02-experiment-tracking/homework-wandb/train.py | 47 | # Define the XGBoost Regressor Mode, train the model and perform prediction | COMMENT |
| MEDIUM | …ts/2023/02-experiment-tracking/homework-wandb/sweep.py | 28 | # Define the XGBoost Regressor Mode, train the model and perform prediction | COMMENT |
| MEDIUM | .github/workflows/cd-deploy.yml | 25 | # Define the infrastructure | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | 04-deployment/batch/score_backfill.py | 10 | def ride_duration_prediction_backfill(): | CODE |
| LOW | 05-monitoring/dummy_metrics_calculation.py | 34 | def calculate_dummy_metrics_postgresql(curr): | STRING |
| LOW | 05-monitoring/evidently_metrics_calculation.py | 65 | def calculate_metrics_postgresql(curr, i): | STRING |
| LOW | 05-monitoring/evidently_metrics_calculation.py | 87 | def batch_monitoring_backfill(): | STRING |
| LOW | …toring/post-evidently-0.7/dummy_metrics_calculation.py | 34 | def calculate_dummy_metrics_postgresql(curr): | STRING |
| LOW | …ng/post-evidently-0.7/evidently_metrics_calculation.py | 69 | def calculate_metrics_postgresql(i): | STRING |
| LOW | …ng/post-evidently-0.7/evidently_metrics_calculation.py | 94 | def batch_monitoring_backfill(): | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | cohorts/2022/03-orchestration/code/model_training.py | 61 | return X_train, X_val, y_train, y_val, dv | COMMENT |