TensorFlow development company
Machine learning models that leave the notebook and hold up in production: Keras training on tf.data pipelines, evaluation sliced by segment, serving through TensorFlow Serving or Vertex AI, and on-device inference with LiteRT or TensorFlow.js.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
From notebook to production with TensorFlow
Taking a model from notebook to production is the job Krapton does as a TensorFlow development company: data pipelines, Keras model training, evaluation, and deployment through TensorFlow Serving, LiteRT on phones and edge devices, or TensorFlow.js in the browser. TensorFlow is a strong choice when you already run TensorFlow models, need mature mobile and edge deployment, use TFX on Vertex AI, or want inference in the browser. For new large language model work, the PyTorch and Hugging Face ecosystem is usually the better starting point, and for tabular data, gradient-boosted trees often beat a neural network outright; we say so when that applies. Our engineers keep preprocessing inside the model with Keras preprocessing layers or tf.Transform to avoid training-serving skew, profile input pipelines before anyone buys more GPUs, and pin seeds, data versions, and package versions so results can be reproduced. Keras 3 models written with backend-agnostic ops can also run on JAX or PyTorch.
TensorFlow development services
Keras model development
Custom and fine-tuned models for vision, text, time series, and recommendations, built with Keras and KerasHub and trained with mixed precision and XLA where the hardware benefits.
Input pipelines with tf.data
Data loading rebuilt around tf.data and TFRecord files with parallel interleave, caching, and prefetching, so accelerators stay busy instead of waiting on disk or Python preprocessing.
Production model serving
Models exported as SavedModel and served with TensorFlow Serving or Vertex AI endpoints, with request batching, version labels for safe rollbacks, and latency monitoring.
On-device and browser inference
Models converted to LiteRT with post-training quantization or quantization-aware training, or to TensorFlow.js, then checked for accuracy loss and latency on target phones or browsers.
TFX and MLOps pipelines
Reproducible training pipelines with TFX components for validation, transformation, training, evaluation, and pushing, orchestrated on Vertex AI Pipelines or Kubeflow, with model cards and lineage.
Legacy TensorFlow upgrades
TF1 graphs and Estimator-era code moved to TensorFlow 2 and Keras 3, deprecated APIs replaced, and the upgraded model checked against the original’s metrics before switching.
Why teams choose Krapton for TensorFlow
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Honest framework recommendations
If PyTorch, a gradient-boosted tree, or a hosted model API would solve your problem better, we recommend it, even when that means less TensorFlow work for us.
Evaluation beyond one metric
Models are judged on held-out data sliced by customer segment, region, or device with TensorFlow Model Analysis, so a good average cannot hide a failing group.
Training-serving skew prevented
Preprocessing lives in the exported model or a shared tf.Transform graph, so features computed at prediction time match those the model saw during training.
Monitoring after launch
Deployed models report prediction distributions, input drift, and latency, with alerts and a retraining path agreed up front, because accuracy can decay as real-world data shifts.
How a TensorFlow project runs with Krapton
01
Problem and data audit
First comes a check on whether ML is needed at all, then label quality and volume, success metrics agreed with your team, and a baseline model to beat.
02
Baseline then iteration
A simple baseline ships first, then experiments are tracked in TensorBoard or MLflow with dataset versions, so every improvement is measured and reproducible.
03
Packaging for deployment
The chosen model is exported, quantized if it runs on devices, load-tested on serving infrastructure, and wrapped with input validation and fallbacks.
04
Launch and monitoring
Rollout starts in shadow mode or on a small share of traffic, compares against the baseline on live data, and adds drift monitoring before full release.
The TensorFlow stack we build with
TensorFlow development: questions answered
What does a TensorFlow development company hand over at the end?
A working model in production, not just a trained file. A TensorFlow development company should deliver the data pipeline, training code, evaluation reports, the exported model, serving or on-device deployment, and monitoring for drift and latency. Krapton also documents how to retrain, which datasets and versions were used, and where the model’s limits are, so your team can maintain it.
Should we use TensorFlow or PyTorch for a new project?
It depends on where the model will run and what you are building. PyTorch dominates research and new LLM work, with most open model releases targeting it first. TensorFlow remains strong for production pipelines with TFX, mobile and edge deployment through LiteRT, and in-browser inference with TensorFlow.js. Keras 3 narrows the gap by running on TensorFlow, JAX, or PyTorch, so model code can stay portable.
Can you migrate our TensorFlow 1 or Keras 2 code?
Yes. TF1 code built on sessions, placeholders, or Estimators is rewritten for eager execution and tf.function. Since TensorFlow 2.16, the keras package means Keras 3, so older Keras 2 code either moves to Keras 3 or pins the tf_keras package for now. Predictions from old and new models are compared on the same test set to confirm the migration changed nothing important.
How do you make TensorFlow models fast in production?
Mostly by removing waste before adding hardware. On the training side, profiling often shows input pipelines starving the GPU, fixed with tf.data parallelism, caching, and prefetching. For inference, the options include request batching in TensorFlow Serving, XLA compilation, mixed precision, quantization for LiteRT, and smaller distilled models. Each change is measured against accuracy, because speed that costs too much quality is not a win.
Where does the budget go in a TensorFlow project?
Most of it goes to data and deployment rather than model code. The main drivers are how much cleaning and labeling the data needs, whether training needs GPUs or TPUs over long runs, the deployment target, compliance rules for personal data, and how much monitoring and retraining automation you want. Scoping a proof of concept separately from production work lets you decide with evidence before committing.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.