TFLite Mobile Deployment
On-device ML for iOS and Android using TensorFlow Lite — fast inference without sending data to a server.
We build TensorFlow models for production — mobile deployment with TFLite, serving with TF Serving, and large-scale training on GCP's Vertex AI.
On-device ML for iOS and Android using TensorFlow Lite — fast inference without sending data to a server.
Production model serving with TF Serving — versioned model management, A/B testing and low-latency inference endpoints.
Large-scale distributed training on Google Cloud Vertex AI — custom training jobs with managed infrastructure.
End-to-end ML pipelines with TFX — data validation, transformation, training, evaluation and serving in one managed system.
TensorFlow Recommenders (TFRS) for collaborative filtering, content-based and hybrid recommendation systems.
TFLite quantisation, pruning and clustering — smaller, faster models for edge and mobile deployment.
PyTorch for research and most new training projects. TensorFlow when TFLite mobile deployment, TF Serving ecosystem or Vertex AI integration is a priority.
Keras API (tf.keras) for most work — more readable and faster to iterate. Low-level TF API only for custom training loops that Keras can't express cleanly.
Yes — TFLite is our recommendation for running ML models on iOS and Android without an internet connection.
Yes — Google Cloud TPUs via Vertex AI for large-scale TensorFlow training jobs where TPU cost efficiency beats GPU.
Production-first, mobile-ready and deeply integrated with GCP — TensorFlow for deployment-focused ML.
Tell us what you want to build. We reply within 4 business hours.