Do we need a custom model at all?

Often not. A commercial API frequently solves the problem for less than training something of your own, and we will say so when that is true. Custom models earn their place when the task is specific to your data or has to run without a network connection.

Can a model run inside our mobile app?

Usually yes, within limits. Vision, audio, and text models compress to sizes that ship comfortably inside an app. Very large models still need a server. We assess feasibility against your size and speed targets before any training starts.

How much data do we need?

Less than most people expect, because starting from a pre-trained model and adapting it needs a fraction of what training from scratch would. The quality and labelling of that data matters far more than the raw quantity of it does.

How do we know if the model is any good?

We agree the measure before building, tied to something your business recognizes rather than an accuracy figure alone. A model that is right ninety percent of the time can still be useless if the ten percent it misses is the part that matters.

TensorFlow
Development Agency

Stateside Digitals is a TensorFlow development agency building machine learning models that make it into production, including models small enough to run inside a mobile app with no server involved at all.

TensorFlow is Google’s machine learning framework, used to build, train, and run models. Its particular strength is what happens after training. A lot of machine learning work stops at a notebook producing encouraging numbers, whereas TensorFlow was built assuming the model has to end up somewhere real, answering questions for actual users at volume.

For a business, that distinction matters more than it sounds. A model running on a data scientist’s laptop and a model serving thousands of people inside your product are different engineering problems with different costs. TensorFlow’s tooling is aimed at the second one, including running models directly on phones where there is no server to call at all.

Runs On The Device

No Server Round Trip

Predictions happen on the phone itself, so the feature works offline, answers instantly, and sends none of the user’s data anywhere.

Built For Production

Serving And Pipelines Included

Retraining, versioning, and deployment tooling come with the framework, rather than being assembled from parts once you need them.

Small Enough To Ship

Models Measured In Megabytes

Compression and quantization shrink a trained model to a size that fits inside an app download without users noticing the difference.

On-Device Vision Features

Document scanning, product recognition, and quality checks that run through the camera without uploading anything to be processed.

Real-Time Personalization

Ranking what each user sees first, updated as they behave rather than overnight in a batch job, which is usually where recommendation systems stop being worth their cost.

Forecasting Models

Demand, churn, and capacity predictions built from your own history, delivered where the people making the decisions already work.

Most machine learning features send data to a server, wait for a response, and display the answer. That arrangement holds up until the connection is poor, until the volume makes the per-request bill uncomfortable, or until the data turns out to be something your users would rather not have leaving their device in the first place.

TensorFlow’s on-device runtime lets a trained model ship inside the app and run locally. Photo classification, document scanning, pose detection, and text suggestions all work offline, respond immediately, and cost nothing per use. The real constraint is size, since the model has to fit alongside your app, and that is a decision we make early rather than discover at the end.

We build with TensorFlow and Keras, training on Vertex AI or SageMaker depending on where your data already sits. Production models serve through TensorFlow Serving, with TFX handling pipelines and retraining. On-device work uses the TensorFlow Lite runtime for iOS and Android, and TensorFlow.js where a browser is the target. Experiments are tracked so results stay reproducible.

TensorFlow
Keras
Python
Vertex AI
SageMaker
TensorFlow Serving
TFX
TensorFlow Lite
TensorFlow.js
iOS
Android
Experiment Tracking

Baseline Before Modelling

Evaluated On Your Data

Model Size Budgeted

Retraining Planned

We find out what a handful of simple rules already achieves, because a model has to beat that convincingly before it is worth its cost.

Performance is measured against your own records rather than a public benchmark, since that is the only number that predicts how it behaves in your product.

For on-device work the size limit is agreed before training starts, so nothing is discovered at the end that will not fit inside the app.

Models drift as the world changes, so how and when yours gets retrained is part of the build rather than a problem for next year.

Baseline Before Modelling

Evaluated On Your Data

Model Size Budgeted

Retraining Planned

PyTorch is the stronger choice for developing and training new models, and it is what most research uses. TensorFlow is the better fit when the priority is deployment, particularly on mobile devices. We work in both and pick based on where yours has to run.

TensorFlow
Development Agency