A mature framework with a strong deployment and serving story, particularly on Google Cloud. Its serving and on-device runtimes are still the shorter path for a good deal of production inference.
Existing TensorFlow estates, and production serving where the tooling around the model matters as much as the model. In practice that means inheriting a working system rather than starting one, which is a different job and priced as one.
New research work. The ecosystem has largely moved, and a smaller community around your problem is a real cost. We will say so plainly rather than quietly bill the extra time it costs.
Usually maintenance and serving rather than training — keeping a model that works in production reproducible. That includes knowing when a model has drifted far enough from the data it was trained on to be retired — a decision someone has to own, not a threshold that fires on its own.
Where this appears on the site
We work in TensorFlow, and no case study or service page currently published on this site prints it in its stack. Rather than describe an engagement you cannot check, this space stays empty until one does. Ask us and we will talk you through it directly.
Also in ai and machine learning
Tell us what it is running, what it costs you today, and what you need it to do next. A senior engineer will tell you what we would keep and what we would change.
