LogMark for Data Scientists
Hypotheses during EDA. Experiment decisions. Model intuitions captured before they fade.
The Problem
You're running an exploratory analysis when you notice an anomaly. You have a hunch about what's causing it, but you need to finish the current notebook first. By the time you circle back, the hunch is gone - or worse, you can't remember why it felt important.
Data science workflows generate constant micro-insights: feature interactions during EDA, hyperparameter intuitions during training, unexpected correlations during validation. Most never get recorded because the capture friction is too high relative to the thought's immediacy.
Why LogMark
LogMark captures which application and window you're in. Entries from Jupyter, VS Code, or your terminal include that context automatically. Route hypotheses to experiment folders. Tag with dataset names or model versions. Search later by any of these dimensions.
Workflows
Hypothesis capture during EDA
+churn-model i: customer tenure might be confounded with plan type +churn-model i: check if signup source correlates with early churn
Experiment decisions
+churn-model d: using XGBoost over random forest -- handles missing values natively, team has experience
Observation capture during training
+churn-model b: model overfits badly on segment C -- maybe need stratified sampling
Feature engineering ideas
+churn-model i: interaction feature between tenure and support ticket count
Result logging
+churn-model AUC improved to 0.847 with tenure bucketing #experiment-23
Notation Guide
Example Day
Coming Soon
MLflow Integration
Link captures to MLflow experiment runs. Tag entries with run IDs and LogMark connects the reasoning to the metrics automatically.