TensorFlow development for production ML: model training, TFX pipelines, and serving at scale, including maintenance of existing TensorFlow estates.
Inherited models with no owners
Companies inherit TensorFlow models from departed teams: they still score every transaction, but nobody can retrain them, the data pipeline feeding them shifted, and accuracy decays silently. Orphaned models are a liability with a countdown attached. We adopt them, document them, and restore the retraining loop.
What we build with TensorFlow
- Model development and retraining pipelines on TensorFlow and Keras
- Adoption and documentation of inherited models running in production
- Serving infrastructure with TensorFlow Serving or cloud-native endpoints
- Mobile and edge deployment through TensorFlow Lite where needed
How we work
Recover reproducibility first: data, code, and weights under version control
Establish evaluation baselines before changing anything
Automate retraining so freshness stops depending on heroes
Monitor prediction drift alongside system health
Typical stack
Frequently asked questions
For new research-adjacent work we usually reach for PyTorch. TensorFlow earns its place with existing estates, serving-scale deployments, and mobile targets. The right answer depends on where the model has to live.
Yes, that is one of the most common ways we start. We reverse-engineer the training setup, restore reproducibility, and put a retraining pipeline around it before the silent decay becomes visible.