Create Deployment
curl --request POST \
--url https://api.landing.ai/v1/projects/{project_id}/deployments \
--header 'Content-Type: application/json' \
--data '
{
"name": "<string>",
"modelId": "<string>",
"threshold": 123
}
'import requests
url = "https://api.landing.ai/v1/projects/{project_id}/deployments"
payload = {
"name": "<string>",
"modelId": "<string>",
"threshold": 123
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({name: '<string>', modelId: '<string>', threshold: 123})
};
fetch('https://api.landing.ai/v1/projects/{project_id}/deployments', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));{
"data": {
"id": "<string>",
"predictionUrl": "<string>",
"name": "<string>",
"modelId": "<string>",
"threshold": 123
}
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}Deployment
Create Deployment
This endpoint creates a deployment, which is an instance for running a model and performing predictions.
To create a deployment, you only need to provide a unique name. Upon successful creation, the endpoint will return an HTTP URL where you can send requests for predictions (also known as inferences).
The deployment name cannot be changed after creation.
Here’s a breakdown of the deployment creation options:
Model (Optional): You can either:
- Provide the model data during deployment creation, making it ready to use immediately.
- Upload a model to the deployment later and then use it for predictions.
Threshold (Optional):
- Specify a custom threshold value for your predictions.
- If not provided, the default value used during model training will be applied.
POST
/
v1
/
projects
/
{project_id}
/
deployments
Create Deployment
curl --request POST \
--url https://api.landing.ai/v1/projects/{project_id}/deployments \
--header 'Content-Type: application/json' \
--data '
{
"name": "<string>",
"modelId": "<string>",
"threshold": 123
}
'import requests
url = "https://api.landing.ai/v1/projects/{project_id}/deployments"
payload = {
"name": "<string>",
"modelId": "<string>",
"threshold": 123
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({name: '<string>', modelId: '<string>', threshold: 123})
};
fetch('https://api.landing.ai/v1/projects/{project_id}/deployments', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));{
"data": {
"id": "<string>",
"predictionUrl": "<string>",
"name": "<string>",
"modelId": "<string>",
"threshold": 123
}
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}⌘I