TensorFlow experts
End to end TensorFlow services covering model design, training, optimisation and production deployment.
We partner with your teams to turn research prototypes into maintainable, monitored machine learning products.
What is TensorFlow
TensorFlow is an open source machine learning framework used for deep learning, computer vision and large scale training.
It offers flexible APIs for research and production, supporting GPUs, TPUs and on device deployment.
We design TensorFlow workflows with data governance, experiment tracking and deployment automation.
CNNs, RNNs and transformer based models tuned for your use case.
Data ingestion, augmentation and distributed training on cloud infrastructure.
TensorFlow Serving, TFX or custom APIs with autoscaling and monitoring.
Quantisation, pruning and edge deployment for mobile or embedded devices.
Why TensorFlow
- Backed by Google with mature tooling
- Supports research experimentation and production deployment
- Great for vision, NLP and time series forecasting
- Integrated ecosystem with TFX, TensorBoard and Vertex AI
TensorFlow projects
Examples of machine learning initiatives we deliver using TensorFlow.
Image classification, detection and segmentation for industry specific needs.
Text classification, summarisation and sentiment analysis pipelines.
Demand, finance and supply chain forecasting with deep learning models.
Personalised product or content recommendations with scalable serving layers.
On device models for mobile, IoT or embedded hardware.
TFX pipelines, experiment tracking and deployment automation.
When TensorFlow is the right choice
- You need scalable deep learning with GPU or TPU acceleration
- Projects require advanced computer vision or NLP capabilities
- Teams want an ecosystem with production ready tooling
- You need to deploy models across cloud, edge and mobile targets
When to consider another framework
- PyTorch may suit research teams needing dynamic computation graphs.
- Classical machine learning could be enough for structured datasets.
- Managed AI services might cover standard use cases quickly.
- LLM APIs could deliver conversational features without custom training.
TensorFlow vs PyTorch
| Criterion | TensorFlowEcosystem | PyTorchResearch |
|---|---|---|
| Execution | Graph execution with tf.function and TFX | Dynamic eager execution with strong research adoption |
| Production | TFX pipelines, Serving and Vertex AI integration | TorchServe, ONNX and custom deployment |
| Community | Large enterprise adoption and tooling | Favoured by research and open source community |
| Use cases | Vision, NLP, large scale production | Research, prototyping, custom architectures |
| Edge support | TensorFlow Lite for mobile and edge | PyTorch Mobile and TorchScript |
We help select TensorFlow or PyTorch based on your team, roadmap and deployment targets.
Accelerate TensorFlow delivery
We provide strategy, engineering and MLOps to launch dependable TensorFlow products.
No obligation. We respect IP boundaries and remove shared artefacts on request.