Model architecture for ML engineers who think about inference from day one.
ML engineers design model architecture with serving in mind — latency budgets, memory constraints, quantization tradeoffs, batch vs. online inference. The whiteboard is where all of that gets designed. BoardSnap captures it before the architecture decisions drift into folklore.
Why ml engineers love this workflow
ML engineers approach model architecture differently than data scientists: the question isn't just 'does it work?' but 'does it work within the latency budget, memory limit, and throughput requirement of production?' These constraints shape every architectural decision — and they need to be documented alongside the architecture itself.
BoardSnap reads the architecture diagram, the serving constraints, the quantization and optimization annotations, and the deployment configuration notes and produces a structured architecture document that captures both the model design and its production requirements.
The exact flow
- Draw the model architecture with serving in mind
Sketch the full model — input preprocessing, model layers, output postprocessing. Annotate each section with latency and memory requirements.
- Note quantization and optimization decisions
Mark where quantization, pruning, or distillation will be applied. Show the precision tradeoffs — INT8 vs. FP16 vs. BF16.
- Define the serving configuration
Write the inference target — batch vs. online, GPU vs. CPU, max latency P99. These are the constraints the architecture must meet.
- Map the input/output contract
Define exactly what the model receives and what it returns — feature names, shapes, types, and ranges. This becomes the API contract.
- Snap the architecture board
Open BoardSnap and capture. The model architecture, serving constraints, and I/O contract are all documented.
What you'll get out of it
- Serving constraints are documented alongside the architecture — not discovered at deployment
- Quantization and optimization decisions are captured with their rationale
- The I/O contract is documented before the serving infrastructure is built
- New ML engineers understand the production architecture without reading all the code
- Architecture versions are searchable for comparison as the model evolves
Frequently asked
How is ML engineer model architecture different from data scientist model architecture?
ML engineers focus on the production serving requirements alongside the model design — latency, throughput, quantization, hardware constraints. The BoardSnap summary for an ML engineer captures both the model structure and its production operating constraints.
Can BoardSnap read latency budgets and hardware constraint annotations?
Yes. Numeric annotations with units — '15ms P99,' '2GB memory,' '100 RPS' — are captured as written and associated with the model component they're annotating.
How does the architecture document help with model review?
The structured document is the basis for a production readiness review. Reviewers can check that every layer meets its latency budget, that the I/O contract is complete, and that optimization decisions are justified — without needing to read the raw code.
Can I use the architecture document for the model card?
Yes. The BoardSnap architecture summary captures the technical specification section of the model card. Add intended use, limitations, and bias analysis to complete it.
ML Engineers: try this on your next model architecture.
Three taps. Action items in your hand before the room clears.