TensorFlow at Production Scale
Build neural networks that generalize. From experimentation to production inference serving, we architect TensorFlow systems that are reliable, performant, and maintainable.
What we deliver with TensorFlow
Reproducible training pipelines
Versioned datasets, pinned dependencies, and configuration-driven runs, so a model can be retrained identically months later.
Distributed training
Multi-GPU and multi-worker strategies with input pipelines that keep accelerators busy instead of waiting on data.
Model packaging and versioning
SavedModel artefacts, registry entries, and lineage from data snapshot to endpoint, so every prediction traces back to a known model.
Serving and latency budgets
TF Serving or a managed endpoint with batching, warm instances, and measured p95 latency against the budget the product actually needs.
Evaluation and drift monitoring
Offline evaluation on held-out sets plus production checks on input distributions, so degradation is caught before customers report it.
Serving skew prevention
The same feature transforms run in training and inference, tested explicitly, because skew is the quietest way a good model turns bad.
How we work with TensorFlow
We treat training and serving as one system. Feature transformations are written once and reused, evaluation sets are locked before tuning begins, and the deployment path is exercised early with a trivial model so pipeline problems surface in week one rather than after the research is finished.
GPU spend gets the same attention as accuracy. Batch sizing, mixed precision, and preprocessing placement are tuned together, because a model that is 2% better and 40% more expensive per prediction is usually the wrong trade for the business.
TensorFlow in production: where it breaks
The same handful of problems show up on almost every engagement. Naming them early is cheaper than discovering them at 2am.
Training-serving skew
Preprocessing differs between the notebook and the endpoint, so live accuracy drops. Shared transform code plus a parity test closes the gap.
Notebooks promoted to production
Unpinned code paths break on the next dependency change. We convert runs into scheduled jobs with versioned inputs.
Data pipelines starving the GPU
Accelerators idle waiting on input. Proper tf.data pipelines with prefetch and parallelism recover most of the wasted spend.
When to use TensorFlow
Forecasting and computer-vision models heading into production.
Teams needing on-premise or edge inference with TF Serving.
Regulated environments where every model decision must be explainable and reproducible.