TensorFlow development for production machine learning
TensorFlow is an open-source machine learning platform from Google, with Keras as its high-level API. It is well established in production environments and offers tools for serving models at scale and running them on mobile devices and in the browser.
TensorFlow has a mature deployment story: TensorFlow Serving for servers, TensorFlow Lite for mobile and embedded devices, and TensorFlow.js for the browser. That makes it a good fit when models must run on-device or when an organisation already has TensorFlow pipelines. For new research-led work and fine-tuning open models, PyTorch is now more common. For many business prediction tasks on tabular data, scikit-learn remains simpler and entirely adequate.
What we build with TensorFlow
- On-device models for mobile apps
- Browser-based machine learning
- Image and audio classification
- Maintaining existing TensorFlow pipelines
- Scalable model serving
TensorFlow questions
Can TensorFlow models run inside a mobile app?
Yes. TensorFlow Lite runs optimised models directly on iOS and Android devices, which keeps data on the device and works offline.
What is Keras?
Keras is a high-level API for defining and training neural networks. It is the standard way to build models in TensorFlow.
Can you maintain or upgrade our existing TensorFlow system?
Yes. We review the current models, pipelines and serving setup, update dependencies safely and improve testing so changes can be made with confidence.
Can TensorFlow models run in a web browser?
Yes. TensorFlow.js runs models in the browser, which keeps data on the user's device and removes server inference costs for suitable models.
How are TensorFlow models served in production?
TensorFlow Serving or a managed cloud service hosts models behind an API, with support for versioning so new models can be rolled out and rolled back safely.
How do you make a model small enough for mobile?
We use quantisation and pruning to shrink models and speed up inference, then check that accuracy remains within the agreed limits on real devices.
What is TFX?
TensorFlow Extended is a set of components for production machine learning pipelines, covering data validation, training, evaluation and deployment in repeatable steps.
How do you validate a TensorFlow model before release?
We evaluate on held-out data, compare against the current production model and check performance across important segments so improvements are not hiding regressions.
Can TensorFlow models be converted to PyTorch?
Conversion is sometimes possible through formats such as ONNX, but it rarely makes sense on its own. We recommend moving only when there is a clear benefit, such as access to newer open models.
How do you watch a TensorFlow model after deployment?
We log predictions and input statistics, compare them with training data to detect drift and schedule retraining when accuracy or data patterns change.
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