Model Deployment
Model Deployment is the final step in the machine learning lifecycle. It takes a versioned artifact from the Model Registry and wraps it into a high-performance, scalable endpoint ready to serve real-time predictions.
Deploying a Registered Model
To deploy a model, you need the id of the logged model (which you obtained in the previous step). The deployment process allocates the necessary computational resources (CPU, RAM, or GPU) and sets up the inference runtime.
Create a Deployment
Use the deployment create command to launch your model. You must specify the model ID and the desired serving size.
fathom intelligence machine-learning deployment create logged-model --model-id 6174cc98-55fb-4818-9370-f75cafade62e --name "iris-classifier" --description "Production endpoint for Iris flower classification" --serving-size small
| Option | Requirement | Description |
|---|---|---|
| –model-id | Required | The UUID of the model from the registry. |
| –name | Required | A unique name for your deployment. |
| –serving-size | Optional | Resource tier: small, large, or extra-large. |
| –serving-gpu | Optional | Attach a GPU for heavy models (nvidia-l4, nvidia-l4-2x). |
Tag deployment
Use the deployment tag to tag your deployment. You must specify the deployment ID. It is possible to remove tags using deployment untag.
Monitoring Deployment Status
Deployments happen asynchronously. After creating one, you should monitor its state to ensure it transitions to running:
fathom intelligence machine-learning deployment list
Output example of a command run with --watch option:
id | created_at | name | kind | description | status | state | tags
--------------------------------------+--------------------------------+----------------------+--------------------------------------------+----------------------------------------------------+---------+--------------
379f103f-45cd-4c00-aec3-0fa4af756cae | 2026-03-25 08:06:17.003811 UTC | iris-classifier | logged-models | Production endpoint for Iris flower classification | pending | N/A | production
id | created_at | name | kind | description | status | state | tags |
--------------------------------------+--------------------------------+-----------------+---------------+----------------------------------------------------+---------+---------------
379f103f-45cd-4c00-aec3-0fa4af756cae | 2026-03-25 08:06:17.003811 UTC | iris-classifier | logged-models | Production endpoint for Iris flower classification | running | hot | production |
Resource Sizing
For the Iris Classifier (ONNX), a small serving size is more than sufficient. Choose large or attach a GPU only for complex models.Updating a Deployment
Once a deployment is running, you can update it to point to a new version of your model (e.g., a newly trained logged-model-id) or change its resource allocation (e.g., upgrading from small to large).
The platform performs a rolling update, ensuring that your endpoint remains available while the new model version is being provisioned.
fathom intelligence machine-learning deployment update <DEPLOYMENT_ID> logged-model <OPTIONS>
| Option | Description |
|---|---|
| –model-id | The new Logged Model UUID from the registry. |
| –name | Update the display name of the deployment. |
| –description | Update the deployment’s metadata/description. |
| –serving-size | Scale resources (small, large, extra-large). |
| –serving-gpu | Change or add a GPU accelerator. |
Example: Update Logged Model
To promote a new model version to an existing deployment, use the update logged-model command. You will need the Deployment ID and the new Model ID.
fathom intelligence machine-learning deployment update 3cdec2ec-f51e-420c-937a-6c65af770084 logged-model --model-id 93096f6a-3a8a-4315-bc18-615ef72c7bcc
Model Inference
Once your deployment is in the running and hot state, you can begin making predictions. Fathom Intelligence supports three primary inference modes depending on your model type: General Tensor Inference, Chat Completions, and Embeddings.
General Tensor Inference (V2 Protocol)
This mode is used for classic ML models (Scikit-learn, ONNX, XGBoost) and computer vision. It follows the NVIDIA Triton V2 Predict Protocol.
Pipe via Standard Input (Recommended for Scripts)
You can pipe a JSON payload directly into the CLI. This is ideal for integration with tools like jq or automated data pipelines.
echo '{
"inputs": [
{
"name": "float_input",
"shape": [1, 4],
"datatype": "FP32",
"data": [7.0, 3.2, 4.7, 1.4]
}
],
"outputs": [
{
"name": "label"
}
]
}' | fathom intelligence machine-learning deployment infer <DEPLOYMENT_ID> --data -
Inline JSON Payload
For quick manual testing, you can pass the full JSON object directly as a string.
fathom intelligence machine-learning deployment infer <DEPLOYMENT_ID> --data '{
"inputs": [
{
"name": "float_input",
"shape": [2, 4],
"datatype": "FP32",
"data": [5.1, 3.5, 1.4, 0.2]
}
],
"outputs": [
{
"name": "label"
}
]
}'
Protocol Compatibility
MLflow ONNX Models (like the Iris Classifier we registered earlier) strictly support the Triton Inference Protocol via the infer command.
Generative commands like chat and embed are reserved for LLMs and specialized transformers (e.g., from Hugging Face), which will be covered in the following sections.