TENSORFLOW DEVELOPMENT SERVICESTensorFlow Development Services for Models in Production
We build and ship TensorFlow models for products that need predictions in a live app — image checks, text, forecasts and similar work. That includes training, packaging and an API your team can call. If you need a new TensorFlow project or help with one you already have, we can help you plan it and deliver it.
import tensorflow as tf model = tf.keras.Sequential([ tf.keras.layers.Dense(16, activation='relu'), tf.keras.layers.Dense(1),])11+
TensorFlow engineers on staff
24+
TensorFlow projects shipped
6 yrs
TensorFlow experience
95%
Client satisfaction
About TensorFlow
TensorFlow is an open-source library for machine learning, mainly used with Python. You train a model on examples, then use that model to make predictions. Keras is the usual way to define those models in TensorFlow.
Companies choose it when they want a path from a notebook to something that runs in production — on a server, in Docker, or on a device with TensorFlow Lite. It sits next to Python APIs, PostgreSQL or other product data, not as a separate science project.
A model is a program that learned patterns from data. Training is that learning step. Inference is using the trained model on new inputs, such as a photo or a sentence from a user.
Why Choose TensorFlow
A full path to production
Training, saving, serving and Lite for mobile are part of the same family. You are not stitching five unrelated tools for the basics.
Works with Python products
Most TensorFlow work lives beside Django, FastAPI or other Python services. The model can sit behind the same API as the rest of the product.
On the server or on a device
TensorFlow Serving and TensorFlow Lite cover cloud APIs and on-device checks when the phone should not send every image to a server.
Clear enough for a product team
Keras keeps many models in a readable form. Data scientists and backend developers can share the same project with less translation.
Fits Docker and your data
A trained model can be packaged with Docker and fed from PostgreSQL, files or an existing Python pipeline.
What We Do With TensorFlow
We plan, train and ship TensorFlow models as part of a real product. New work or existing code. The aim is something your API and your team can run, not only a notebook.
Train and evaluate models
We build Keras and TensorFlow models for classification, detection or forecasts, with a clear split between train, check and live use.
APIs around the model
We wrap inference in a Python API so a web or mobile app can send input and get a result without talking to the training code.
TensorFlow Lite
When the check should run on a phone, we convert and test Lite models for Flutter or native apps.
Data and pipelines
We connect training to the files or databases you already have, with repeatable steps so a retraining run is not a mystery.
Docker and serving
We package the model for a server, with the same image in testing and live where that helps.
Improve existing TensorFlow
If a model is slow, outdated or stuck in a notebook, we can tidy it and make it easier for the next person to run.
Benefits for Your Business
Predictions inside the product
The model is called from the same kind of API as the rest of the app. Users do not wait on a separate science workflow.
A path off the laptop
Training and serving are written down. Retraining is a process, not a one-off on someone’s machine.
Room for mobile later
Lite is there if you later need on-device checks. You do not have to start a new ML stack for the phone.
Easier to hand over
Python, Keras and Docker are skills a product team can follow. The work is not locked in one person’s notebook.
Ready to Talk?
If you are planning a TensorFlow project, or you already have one that needs work, send us a short note about what you want to build. We will review it and come back with an honest next step.
Contact us