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TensorFlow Machine Learning Development
Maintaining and extending TensorFlow pipelines, and deploying models to mobile and browser, taken on per engagement.
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What TensorFlow is
TensorFlow is Google's open-source machine learning framework, with Keras as its high-level model-building API. Its ecosystem is broad: TensorFlow Serving for production inference, TensorFlow Lite for mobile and embedded devices, and TensorFlow.js for running models in the browser.
For new research and fine-tuning work, much of the field has moved to PyTorch. TensorFlow stays relevant where it is already deployed, where its deployment tooling for mobile, edge or the browser is the deciding factor, or where a team has a mature pipeline whose rewrite would cost more than it returns.
Suitability
And when it is not. Choosing the wrong tool costs more than any implementation detail.
Good fit for
- Extending or maintaining an existing TensorFlow and Keras codebase rather than rewriting it
- On-device inference on Android, iOS or embedded hardware through TensorFlow Lite
- Running small models directly in the browser with TensorFlow.js so data never leaves the user's device
- Established production-serving setups already built around TensorFlow Serving
Consider alternatives for
- Greenfield research or fine-tuning of recent open models, where PyTorch has the larger share of tooling and pretrained releases
- Teams without TensorFlow familiarity choosing it only because of its name recognition
- Problems that a hosted model API or a simpler classical model already solves
In practice
01
Pipeline maintenance and upgrades
Taking over an existing TensorFlow training and serving pipeline, adding tests and tracking, and upgrading it where that is safe — without a rewrite that would reset accuracy.
02
On-device and browser inference
Converting and optimising models for mobile and browser targets, with a measured check that accuracy survives quantisation and that latency is acceptable on real devices.
Considerations
Practical notes from running this in production.
- 01
We take TensorFlow on per engagement and most of our new model work is in PyTorch. We would rather say so than present equal depth in both.
- 02
Converting a model for mobile or browser can change its behaviour. Quantisation and unsupported operations need an accuracy check on the converted artefact, not just the original.
- 03
Version upgrades between major TensorFlow releases can break custom ops and saved models. Pin versions and keep a reproducible environment before attempting an upgrade.
- 04
On-device models ship inside your app, so model updates follow your release process unless you add a separate download mechanism.
FAQ
Should we move our TensorFlow system to PyTorch?
Only if there is a concrete reason, such as needing a model or tool that only ships for PyTorch. A working pipeline with tests is worth more than a framework preference, and a migration has real risk. We assess the actual friction first and often recommend leaving it in place.
Can a model run on the phone instead of a server?
Often yes for small vision, audio or classification models, using TensorFlow Lite or an equivalent. It improves privacy and works offline, but limits model size and needs testing on the range of devices your users own. Large language models are generally not a fit for ordinary phones today.
How long does a TensorFlow development project take?
It depends on scope. A focused first release is usually weeks rather than months, while larger platforms are delivered in phases. We give a written estimate after a short discovery conversation and agree milestones before work starts.
How is TensorFlow development work priced?
We work either to a fixed scope with agreed milestones or on a time-and-materials basis, whichever suits how well the requirements are known. You receive the estimate and its assumptions in writing before committing.
Who owns the code and the intellectual property?
Ownership terms are agreed in writing before work begins, so there is no ambiguity about what you receive and what you may reuse.
By industry
Where we apply TensorFlow. Tiles marked "Guide" open a dedicated write-up; the rest open the industry page.
Let’s talk about your TensorFlow project
Tell us what you are trying to build or fix. We will come back with scope, approach and an honest view on cost and timeline.