TensorFlow Development – Deep Learning, Training & Production Inference
We use TensorFlow and the Keras API for trainable models, reproducible experiments and reliable serving – together with Python and your data pipelines.
TensorFlow Development – Deep Learning, Training & Production Inference Below you will find use cases, services and answers to common questions.
“TensorFlow is tooling, not product—without data pipelines, model versioning and monitoring every model stays a demo.”
Björn Groenewold, CEO, Groenewold IT Solutions
Where we use TensorFlow
From image classification and object detection to text and time-series models and edge deployment: TensorFlow spans training (including GPU/TPU), export (SavedModel) and inference (TensorFlow Serving, TF Lite). We structure datasets, metrics and model versions so QA, audits and iteration stay traceable.
Training & evaluation
Reproducible runs, validation splits and monitoring for drift – aligned with your KPIs.
Serving & integration
APIs, batch jobs or realtime paths; integration with ERP and data platforms.
Stack combinations: Python & TensorFlow, Python & PostgreSQL.