Source: https://daphnislabs.com/services/tensorflow-development

# TensorFlow Development

TensorFlow model development, training pipelines and deployment integration for teams building or maintaining neural-network applications.

[Discuss your project](https://daphnislabs.com/contact?service=tensorflow-development) [Try the example](https://daphnislabs.com/services/tensorflow-development#worked-example)

`[1, 224, 224, 3]`

**SavedModel**

Server Device

Same preprocessing contract

## Tools & technologies

-   TensorFlow
-   Keras
-   tf.data
-   Compatible serving runtime

## One model, several deployment constraints.

### Model development

Implement and evaluate a task-specific neural network with reproducible inputs.

### Inference integration

Export a model and connect it to a compatible server or device runtime.

### Existing pipelines

Diagnose training instability, input bottlenecks and export incompatibilities.

## An export your target runtime can load.

-   **TensorFlow training code**
-   **Versioned model export**

-   **Numerical and runtime checks**
-   **Environment and deployment specification**

## Move a classifier into a product

Capability Example

A trained image classifier must serve the same predictions outside its training notebook.

### Freeze

### Export

### Compare

Illustrative data. Nothing is sent to an external system.

Example workspace 1 / 3

### Saved model

```
input: image tensor
shape: [1,224,224,3]
checkpoint: reviewed
```

Record preprocessing, input shape and label order with the model.

### Conversion check

```
supported operators: checked
quantization: compared
reference outputs: retained
```

Package the model for the agreed inference runtime.

### Device package

```
runtime: LiteRT
input preprocessing: included
device measurements: recorded
```

Check exported predictions against reference inputs before integration.

A deployable model with a tested input contract and measured runtime behavior.

Follow the record through the next step

## Measure on the device that matters.

### Version alignment

Training, export and serving versions must support the operators the model uses.

### Device constraints

Compression or quantization can change predictions and needs a separate accuracy check.

## Questions before we start.

### Can you maintain an older TensorFlow project?

Yes. We first reproduce its environment and outputs, then plan compatible upgrades rather than changing dependencies blindly.

### Is TensorFlow always the best choice?

No. Existing assets, deployment targets and team tooling determine the framework choice; the project can begin with a comparison.

[Deep Learning Development](https://daphnislabs.com/services/deep-learning) [Computer Vision Development](https://daphnislabs.com/services/computer-vision) [AI Inference Engineering](https://daphnislabs.com/services/ai-inference-engineering)

## Bring your starting point.

Bring the training code, model export and the runtime where inference must run.

[Discuss your project](https://daphnislabs.com/contact?service=tensorflow-development) [Explore related services](https://daphnislabs.com/services/deep-learning)