Experiment tracking for ML engineers who know what they've already tried.
When ML experiments multiply, the whiteboard becomes the experiment comparison surface — runs, configurations, metric results, and the decision about what to try next. BoardSnap reads the comparison board and turns it into a structured experiment log before the team moves on.
Why ml engineers love this workflow
ML experiment tracking tools like MLflow and Weights & Biases capture the quantitative output. But they don't capture the qualitative analysis — why this run outperformed that one, what configuration changes are worth exploring next, which experiments were abandoned and why. That analysis happens at the whiteboard.
BoardSnap reads the experiment comparison board, the configuration differences between runs, the performance analysis, and the 'next experiment' decisions and produces a structured experiment analysis document. The quantitative and qualitative tracking work together.
The exact flow
- Lay out experiment runs on the whiteboard
List each experiment with its key configuration differences — architecture changes, hyperparameter changes, data changes.
- Compare metric results
Show the primary metric for each run side by side. Mark the winning run. Note any surprising results or metric trade-offs.
- Analyze the configuration-result relationships
Write what the results tell you — which configuration changes helped, which hurt, which were neutral. This is the qualitative analysis that tools don't capture.
- Design the next set of experiments
Based on the analysis, write the next experiments to run. This is the iterative cycle — the whiteboard drives the next set of runs.
- Snap the experiment tracking board
Open BoardSnap and capture. The run comparison, analysis, and next experiments are documented in one shot.
What you'll get out of it
- Qualitative analysis of experiment results is documented — not just the metrics
- Configuration-result relationships are captured and reasoned about explicitly
- Abandoned experiment directions are documented so they're not re-explored
- The next experiment plan is designed from the analysis, not from intuition
- Experiment tracking boards are searchable for patterns across the research cycle
Frequently asked
How does BoardSnap complement MLflow or W&B for experiment tracking?
MLflow and W&B capture run configurations and metrics automatically. BoardSnap captures the qualitative analysis — why a run performed differently, which configuration changes are worth pursuing, what hypotheses are emerging. Both together give you complete experiment tracking.
Can BoardSnap read a run comparison table with metrics?
Yes. Comparison tables with run names, configurations, and metric values are read and organized in the output. The structured comparison is preserved.
How often should we do whiteboard experiment review sessions?
After every 3-5 experiments, or whenever the team needs to decide which direction to pursue. Snap after each session — the history of analysis drives better future experiments.
Can I share the experiment analysis with stakeholders?
Yes. The BoardSnap summary translates the experiment results and analysis into plain language. Stakeholders can understand the research direction without reading raw metric tables.
ML Engineers: try this on your next experiment tracking.
Three taps. Action items in your hand before the room clears.