TensorFlow Development Services
TensorFlow for models that need to run reliably at production scale
TensorFlow's mature serving infrastructure (TensorFlow Serving) and mobile/edge deployment tooling (TensorFlow Lite) make it a strong choice once a model needs to run reliably in production - on servers or directly on user devices - not just perform well in a training notebook.
We recommend TensorFlow specifically when this production deployment path matters, since its ecosystem is genuinely more mature here than research-focused alternatives.
Why production deployment tooling is TensorFlow's real strength
A model that trains well but is hard to deploy reliably at scale, or that needs to run on a mobile device with limited resources, benefits from TensorFlow's specific tooling for exactly these scenarios.
We assess whether your project's deployment target - server-scale serving, or on-device mobile inference - genuinely benefits from this before recommending TensorFlow specifically.
What's included
- Models built with production deployment as a first-class concern from the start
- TensorFlow Serving infrastructure for reliable server-scale model serving
- TensorFlow Lite optimization where the model needs to run directly on mobile devices
- Realistic accuracy expectations set upfront based on your actual data quality
Our process
1. Assess your deployment target
We confirm whether server-scale serving or on-device mobile inference is your actual requirement.
2. Train and optimize for deployment
The model gets trained and optimized specifically for its intended production environment.
3. Deploy and monitor
We deploy using TensorFlow's production tooling and monitor real-world performance.
Pricing
Custom model development is a substantial investment - pricing depends heavily on data volume and the deployment target complexity. Range: Rs 100,000 - 750,000 - indicative, final quote after discovery. Request a quote or WhatsApp +92 324 2991303.
Industries we serve
Businesses needing machine learning models that run reliably at production scale or directly on mobile devices.