We build with TensorFlow
We use TensorFlow to design, train, and deploy machine learning models at production scale that support your business goals. From architecture prototyping and training pipelines to serving and monitoring, we create products designed to evolve with your needs.
DISCUSS YOUR PROJECTFlexible Execution
We build models with eager execution or optimized graph mode.
Multi-Target Deployment
We deploy trained models to servers, mobile, edge, and browsers.
Full ML Lifecycle
We support training, monitoring, and production model serving.
BENEFITS OF TENSORFLOW technology
We use TensorFlow to train deep learning models, deploy them across servers and devices, and scale ML pipelines.
BUILD
[01]- Design model architectures
- Train deep learning models
- Prepare training datasets
- Tune model hyperparameters
ENGAGE
[02]- Serve real-time predictions
- Support vision and NLP tasks
- Power recommendation systems
- Deploy to mobile and browsers
GROW
[03]- Scale inference workloads
- Monitor model performance
- Retrain with new data
- Extend across TFX pipelines
Our TensorFlow Technology Stack
We combine TensorFlow with Keras, TFX, TensorBoard, and Google Cloud AI Platform for model development, experiment tracking, deployment pipelines, and production monitoring across your ML workflow.
Custom TensorFlow development company
With our TensorFlow development services, we build and deploy deep learning models for computer vision, natural language processing, recommendation systems, and production-scale prediction pipelines, tailored to your boldest business goals. Having years of experience with TensorFlow, our engineers harness its full potential to deliver accurate, performant models across industries and company sizes. Whether it's training a new model from scratch or productionizing and modernizing an existing ML pipeline, we build systems that stay reliable as data and usage grow. Our range of TensorFlow development services spans consulting, model architecture design, training and evaluation, and deployment across TensorFlow Serving, TensorFlow Lite, and TensorFlow.js. We work as your technical partner and ensure complete transparency and comfortable communication throughout the process, bringing deep ML engineering expertise and a rigorous approach to data preparation and problem-solving. All this to make sure your models perform reliably in production and move your business forward.
OUR TENSORFLOW SERVICES
We build, modernize, and support TensorFlow models around your product goals.
Your roadmap is growing faster than your team. Add senior engineering capacity and deliver more without sacrificing quality.
Custom deep learning models designed and trained around your data and business objectives.
Model serving across servers, mobile devices, and browsers with TensorFlow Serving, Lite, and JS.
Automated training pipelines, experiment tracking, and monitoring with TFX and TensorBoard.
Migrate legacy ML pipelines to TensorFlow or optimize existing models for performance and cost.
Meet our TensorFlow experts
A curated selection of senior specialists currently available for new engagements.
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KD
We Turn Technology Into Results
Partner with a team that blends technical precision, creative design, and business insight. We’ll help you launch, scale, and dominate your digital niche.
Frequently Asked Questions
Common questions about how we use TensorFlow and what it can bring to your project. Have a specific requirement?
How does SoftDoes use TensorFlow?
We use TensorFlow to build and deploy deep learning models for computer vision, natural language processing, recommendation systems, and production-scale prediction pipelines. We select the architecture and deployment target around your product's requirements and data.
What types of TensorFlow applications do you build?
We build image recognition systems, NLP models, recommendation engines, and production model-serving pipelines, from early prototypes to systems supporting established products at scale.
Do you use Keras with TensorFlow?
Yes, Keras is our default high-level API for TensorFlow projects. It speeds up model development and keeps architectures easier to iterate on as requirements evolve.
Can you deploy TensorFlow models to mobile and browsers?
Yes. We use TensorFlow Lite for mobile and edge devices and TensorFlow.js for in-browser inference, depending on your latency, privacy, and platform requirements.
How do you monitor TensorFlow models in production?
We use TensorFlow Serving alongside tools like TFX and TensorBoard to track model performance, monitor drift, and manage retraining as new data comes in.
Can you modernize an existing ML pipeline with TensorFlow?
Yes. We can migrate models from other frameworks or legacy pipelines to TensorFlow, or optimize an existing TensorFlow setup for better performance and lower serving costs.
How do you decide whether TensorFlow fits a project?
We look at your data, latency and deployment requirements, existing ML infrastructure, and team experience, then confirm TensorFlow is the right fit before starting development.
