Deep Learning Feasibility — Architecture & Implementation Report

I will assess your use case and deliver a written deep learning feasibility report covering whether deep learning is the right approach versus simpler ML alternatives, the appropriate neural network architecture, training data requirements, compute infrastructure needs, and a realistic effort and cost estimate. Deep learning is frequently over-applied to problems where a gradient boosted tree would deliver better performance with 1% of the compute cost and data requirement — a feasibility consultation prevents the common mistake of choosing deep learning for its perceived sophistication rather than its genuine suitability for the task.

The report covers task type assessment (image, text, audio, tabular, or multimodal), architecture recommendation (CNN, RNN, Transformer, MLP, or other), minimum training data volume estimate, compute requirement assessment (CPU-trainable or GPU-required), transfer learning opportunity identification, and a phased development roadmap.