Model architecture for data scientists who design before they train.
Model architecture sessions on a whiteboard let the team debate layer choices, embedding dimensions, and training strategies visually. BoardSnap reads the architecture diagram and turns it into a structured model spec before anyone opens a notebook.
Why data scientists love this workflow
Designing a model architecture on a whiteboard is faster and more collaborative than sketching in code. You can draw the full pipeline — input features, embedding layers, model layers, output head, training objective — and debate choices in real time.
BoardSnap reads your model diagram, the hyperparameter annotations, the data flow arrows, and the design rationale notes and produces a structured model architecture document. The architecture decisions are captured. The training configuration is documented. The next version of the model has a record of what came before.
The exact flow
- Draw the model architecture
Sketch each layer — input, embedding, hidden layers, output. Label dimensions, activation functions, and layer types.
- Show the data flow
Draw arrows for the forward pass. Annotate where dropout, normalization, or attention happens. Show the loss function at the output.
- Note the training configuration
Write the optimizer, learning rate, batch size, and training objective. These are part of the architecture spec.
- Document design decisions and alternatives considered
Write why you chose this architecture over alternatives. These notes prevent revisiting settled decisions.
- Snap the model architecture board
Open BoardSnap and capture. The full architecture, training config, and design rationale are documented in one shot.
What you'll get out of it
- Model architecture decisions are documented before training begins
- Layer choices and alternatives considered are captured with rationale
- Training configuration is part of the documented spec — not buried in a notebook
- New team members understand the model intent without reading all the code
- Architecture history tracks how the model evolved across versions
Frequently asked
Can BoardSnap read neural network layer diagrams?
Yes. Layer boxes, dimension annotations, and connection arrows are all read. Common notation — 'FC(256),' 'BatchNorm,' 'ReLU,' 'Dropout(0.3)' — is captured as written.
What if we're designing an ensemble or multi-model architecture?
Multi-model architectures with separate branches, ensemble outputs, or stacking configurations read well when each model is clearly labeled and connections are drawn explicitly.
How does the architecture document relate to the model card?
The BoardSnap architecture summary is a key input for the model card. Architecture, training configuration, and design decisions are already captured — use them as the technical section of your model card.
Can I compare architecture designs across experiments?
Yes. Snap the architecture for each major model iteration. Use AI chat to ask 'how did the embedding dimensions change from model v1 to v3.'
Data Scientists: try this on your next model architecture.
Three taps. Action items in your hand before the room clears.