Machine Learning Development

Architecture design starts from the problem, not from a paper: input structure, label availability, latency budget, and deployment target decide whether the right answer is a compact feedforward network, a convolutional model, a transformer, or a fine-tuned pretrained backbone. We prototype fast in PyTorch, establish a baseline with simpler methods first, and only add architectural complexity that earns its place on the validation set.

Training is engineered for reproducibility: versioned datasets, deterministic preprocessing, experiment tracking, hyperparameter search that respects a defined compute budget, and GPU-accelerated runs with checkpointing. Every model ships with a held-out benchmark measured on your data — accuracy, latency, and cost per inference — so the decision to deploy is made on numbers, not enthusiasm.

When the model is the product, we build the platform around it: multi-tenant SaaS with the trained model at its core — accounts, usage metering, an inference API customers integrate, and the dashboards where they see results. The serving layer is designed for SaaS economics from the start: request batching, inference caching, and per-tenant quotas so one heavy customer never degrades another's experience.

Model versioning becomes a product feature here: new versions roll out behind flags, evaluation compares them against the incumbent before full traffic, and rollback is one command when a version underperforms. The doorlist.ai platform runs this pattern in production — AI property matching shipped inside a complete SaaS product, with the model, the API, and the customer experience built as one system.