To deploy a trained machine-learning or deep-learning model to a website, package a reproducible model artifact, choose whether inference runs in the browser or on a server, and give the model a stable interface your website can call. Then validate the deployed artifact, put the service behind HTTPS if it handles requests over a network, and monitor its performance and quality. The right setup depends on the model’s size, privacy needs, traffic, and runtime.
Choose where inference will run
Inference is the step that applies a trained model to new input and returns a prediction. In a web deployment, it can run in the visitor’s browser or on a server your application controls. Neither approach is universally better.
| Consideration | Browser inference | Server inference |
|---|---|---|
| Input privacy | Inputs can remain on the device. | Inputs are sent to your service unless you use other protections. |
| Model confidentiality | The model is downloaded to the client. | Model weights can remain on the server. |
| Compute and cost | Can reduce cloud inference load, but client hardware varies. | Centralizes compute and operations; cloud cost scales with traffic. |
| Model size and capability | Constrained by download size, browser memory, and supported backends. | Better suited to large models and GPU acceleration. |
| Updates | Client caching and version management matter. | Rollouts and rollbacks can be managed centrally. |
Use the browser for suitable client-side workloads
Browser inference is a good candidate when the model fits client constraints and local or offline interaction matters. ONNX Runtime Web provides JavaScript APIs and libraries for running models in web applications; TensorFlow.js is another browser option. Running inference on the client can offload work from cloud servers, but users must download the model and their devices may perform differently.
Use a server when you need centralized control or larger models
Server inference is generally a better fit when the model is large, its weights should stay private, or centralized governance is important. Your website sends an input to an API, the service runs the model, and the API returns a result. The service can use a CPU or GPU-backed runtime; the appropriate choice depends on the workload and must be measured for the specific model.
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Choose a runtime and deployment target
The framework used for training does not by itself determine where the model must run. Export formats and runtimes can offer additional deployment choices, but conversion must be validated against the original model.
| Target | Typical use | What to account for |
|---|---|---|
| TensorFlow Serving | Network-based inference for TensorFlow SavedModels, using REST or gRPC. | Package the SavedModel and configure the service and its model version. |
| ONNX Runtime Web | JavaScript inference in a browser. | Check browser backend support, model download size, memory use, and conversion behavior. |
| TensorFlow.js | Inference in browsers or Node.js. | Select the deployment environment and validate the exported model there. |
| TensorFlow Lite | Native mobile and IoT deployment. | This is a device-oriented target rather than a general website-serving API. |
| NVIDIA Triton or another GPU-backed service | Larger online-inference workloads. | GPU scheduling, capacity, model loading, and scaling add operational complexity. |
TensorFlow Serving is documented as a production-oriented serving system for machine-learning models. TensorFlow’s serving guidance distinguishes network serving with TensorFlow Serving from native mobile and IoT deployment with TensorFlow Lite and browser or Node.js deployment with TensorFlow.js. ONNX Runtime offers a cross-framework route: models can be converted from frameworks such as PyTorch or TensorFlow, then run with a compatible ONNX Runtime target.
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Prepare and validate the model artifact
A deployable artifact is more than a set of weights. The service or browser code must apply the same input preparation and output interpretation expected by the trained model. Record the model and runtime versions, checksum, input schema, preprocessing, postprocessing, and expected output shapes alongside the artifact.
- Preserve preprocessing: Include steps such as tokenization or image and audio normalization where the model expects them.
- Preserve postprocessing: Define how raw model outputs become the result your application displays or acts on.
- Test conversion: For an exported or converted model, compare representative inputs and outputs with the original, using an appropriate numerical tolerance.
- Check compatibility: Look for unsupported operators, tokenizer differences, and input or output shape mismatches in the selected runtime.
- Handle artifacts safely: Treat model files obtained from untrusted sources as risky inputs; inspect and test them safely before production use.
Expose server inference through a stable API
A website should call a defined interface rather than depend on the model’s internal implementation. Version the endpoint and request and response schema so that model changes can be rolled out without silently breaking clients. Return structured errors, set payload limits, and apply authentication and authorization where appropriate. Use HTTPS for network traffic.
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Package and deploy the service
For a server deployment, a container can package the serving runtime and its configuration in a reproducible unit, while the model artifact is mounted or otherwise made available to the service. Pin relevant framework and runtime versions so that deployments can be recreated. TensorFlow’s Docker example demonstrates mounting a SavedModel into a container and exposing its REST endpoint on port 8501.
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For a web deployment, bundle the browser-compatible runtime and model, then manage cache and model versions so clients can obtain updates. In either case, first validate the artifact in a staging environment. Use health checks, gradual or canary rollout where available, and a rollback path tied to explicit model versions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Scale only as the workload requires
A CPU service may be sufficient for one model and traffic pattern; a larger online workload may justify GPU-backed inference. An official Google Kubernetes Engine tutorial demonstrates online inference using one NVIDIA L4 GPU, NVIDIA Triton Inference Server, and TensorFlow Serving. That is an example configuration, not a universal sizing recommendation or performance benchmark.
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Kubernetes can run multiple pod replicas, but replica count alone does not establish capacity. Measure the selected model and design for its resource needs, model-loading behavior, GPU scheduling, and autoscaling requirements. There is no universal latency or cost figure that applies across models and architectures.
Operate the deployment after launch
Monitor both service health and whether the model continues to produce useful results. Track p50, p95, and p99 latency, throughput, queue depth, errors, memory and GPU utilization, and cost. Also define quality or drift indicators appropriate to the application. Set thresholds and response actions for the signals that matter, and keep model-version routing available for controlled updates and rollback.
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