What kind of ML workload are you working on?

Select the primary model type for your use case.

Computer vision

Image classification, detection, segmentation, video, depth, LiDAR-derived vision.

Time series / sensor data

Forecasting, anomaly detection, vibration, IMU, telemetry, sequential signals.

Tabular / structured data

Fraud, risk, ranking, classification, regression.

NLP / small language models

Text classification, small LLMs, rerankers, embedded language systems.

Other

Another ML workload type.

Where does this model run, or where do you need it to run?

Select your deployment environment.

Edge device

Embedded, robotics, camera, sensor, industrial, field hardware.

Mobile / on-device

Phone, tablet, wearable, or other local device.

Cloud GPU infrastructure

AWS, GCP, Azure, or managed GPU inference/training.

CPU / on-prem infrastructure

Private data center, CPU-only inference, local infrastructure.

Hybrid

Mix of cloud, edge, on-prem, or staged deployment.

What is the primary bottleneck today?

Select the most important challenge you're facing.

Accuracy is not high enough

Model quality is the main blocker.

Optimization reduced model quality

Compression or speedup made the model worse.

Inference is too slow

Response time, FPS, or real-time requirements are hard to hit.

Model is too large for hardware

Model size or memory footprint is limiting deployment.

Compute cost is too high

Training or inference is too expensive.

Needs too much training data

Collecting, labeling, or curating enough data is too expensive or slow.

Iteration is too slow

Training, tuning, or deployment cycles take too long.

What have you already tried to improve performance or efficiency?

Select all that apply, or choose "None yet".

Quantization

Reduced precision to speed up or shrink the model.

Pruning

Removed weights, neurons, channels, or layers.

Distillation

Trained a smaller model from a larger teacher.

Smaller architecture

Switched to a smaller or lighter model.

Compiler/runtime optimization

ONNX, TensorRT, TVM, torch.compile, or similar.

Collected or labeled more data

Added more examples to improve model quality.

Data augmentation or synthetic data

Used augmentation, generated data, or synthetic examples.

Active learning or hard-example mining

Prioritized examples, edge cases, or failure modes for labeling.

None yet

You have not tried optimization or data expansion yet.

Which follow-up best describes the tradeoff you're facing?

Select the option that best matches your situation.

Optimization hurt quality significantly

Compression, speedup, or a smaller model materially hurt performance.

Optimization hurt quality somewhat

Efficiency improved, but quality declined.

More data is too expensive or slow

Collecting, labeling, or cleaning additional data is the blocker.

The right data is scarce

You do not have enough representative examples, edge cases, or rare events.

Augmentation or synthetic data has not closed the gap

Data tricks helped some, but model quality is still not where it needs to be.

No obvious quality loss yet

Quality has held up, but you still want better performance or efficiency.

Not sure

You have not measured the tradeoff clearly.

What metric matters most for this workload?

Select the primary metric you track.

What is the current value for that metric?

Enter your current baseline (e.g., 0.86, 120ms, 450MB).

What stage or scale best describes this model?

Select your current deployment stage.

Research / prototype

Early exploration or internal experimentation.

Production pilot

Limited deployment or active evaluation.

Production model

Already deployed or near deployment.

Edge fleet / high-volume deployment

Many devices, high inference volume, or distributed deployment.

Enterprise platform

Shared ML platform, many teams, or multiple models.

Anything else we should know?

Optional: Share any additional context about your model or deployment.

How can we reach you?

We'll send your results and may follow up with relevant insights.

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