Use AI to Deliver Actionable Insights from your Data

Clarifai puts machine learning in the hands of analysts and data scientists to deploy critical AI applications at scale.

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Tightly integrated data prep, AI resources and model development in one AI Lake

Use a unified platform with unlimited customization to turn your image, video, text and audio data into knowledge.

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Easily label and organize your data

Label and organize your data faster using AI-automated data labeling, data augmentation, denoising and deduping tools. 

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Build customized models faster

Quickly prototype ideas, design models, manage versions and run repeatable evaluations. Use transfer learning for quick training or optimize hyperparameters for optimal performance.

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Easy Deployment

Simplify your model deployment

Deploy your AI models anywhere you need them. Use our cloud API, web-based UI or deploy on-premise or at the edge. We maintain the infrastructure, so you don’t have to.

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Faster Insights

Gain data insights faster

Experience low latency, high throughput inference. Use one platform designed for production AI at scale. Clarifai offers a scalable, multi-model inference system that automatically takes care of load balancing to ensure performance.

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Robust Evaluation and Benchmarking

Converge on optimal AI solutions

Manage your AI development with version control and evaluation tools specifically designed for AI. Measure the impact of your work consistently and repeatedly across every version of your models.

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Start building high-performance models today

Get started with 1,000 free operations each month. Request a free API key and start building today.

response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="{THE_MODEL_ID}",
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ]
    ),
    metadata=metadata
)
print("Predicted concepts:")
for concept in response.outputs[0].data.concepts:
    print(concept.name + " " + str(concept.value))
curl -X POST
    -H 'Authorization: Key YOUR_API_KEY'
    -H "Content-Type: application/json"
    -d '
    {
      "inputs": [
        {
          "data": {
            "image": {
              "url": "https://samples.clarifai.com/metro-north.jpg"
            }
          }
        }
      ]
    }'
    https://api.clarifai.com/v2/models/e0be3b9d6a454f0493ac3a30784001ff/outputs

 

MultiOutputResponse response = stub.postModelOutputs(
PostModelOutputsRequest.newBuilder()
.setModelId("aaa03c23b3724a16a56b629203edc62c")
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setImage(
Image.newBuilder().setUrl("YOUR_IMAGE_URL")
)
)
)
.build()
);
const request = new service.PostModelOutputsRequest();
request.setModelId("aaa03c23b3724a16a56b629203edc62c");
request.addInputs(
    new resources.Input()
        .setData(
            new resources.Data()
                .setImage(
                    new resources.Image()
                        .setUrl("https://samples.clarifai.com/dog2.jpeg")
                )
        )
)
[$response, $status] = $client->PostModelOutputs(
new PostModelOutputsRequest([
'model_id' => 'aaa03c23b3724a16a56b629203edc62c',
'inputs' => [
new Input([
'data' => new Data([
'image' => new Image([
'url' => 'https://samples.clarifai.com/dog2.jpeg'
])
])
])
]
]),
$metadata
)->wait();
var GeneralModelId = "aaa03c23b3724a16a56b629203edc62c"
response, err := client.PostModelOutputs(
ctx,
&api.PostModelOutputsRequest{
ModelId: GeneralModelId,
Inputs: []*api.Input{
{
Data: &api.Data{
Image: &api.Image{
Url: "https://samples.clarifai.com/dog2.jpeg",
},
},
},
},
},
)
const GENERAL_MODEL_ID: &str = "aaa03c23b3724a16a56b629203edc62c";

let request = service::PostModelOutputsRequest {
model_id: GENERAL_MODEL_ID.to_string(),
inputs: RepeatedField::from(vec![resources::Input {
data: SingularPtrField::some(resources::Data {
image: SingularPtrField::some(resources::Image {
url: "https://samples.clarifai.com/dog2.jpeg".to_string(),
..Default::default()
}),
..Default::default()
}),
..Default::default()
}]),
..Default::default()
};
string GENERAL_MODEL_ID = "aaa03c23b3724a16a56b629203edc62c";

PostModelOutputsRequest request;
request.set_model_id(GENERAL_MODEL_ID);

Input* input = request.add_inputs();
Data* data = input->mutable_data();
Image* image = data->mutable_image();
image->set_url("https://samples.clarifai.com/dog2.jpeg");

MultiOutputResponse response;
grpc::Status status = stub->PostModelOutputs(context.get(), request, &response);

if (!status.ok()) {
cout << "Failure: " << status.error_code() << " " << status.error_message() << endl;
exit(1);
}

if (response.status().code() != status::StatusCode::SUCCESS) {
cout << "Error response: " << response.status().code() << " " << response.status().description() << " " << response.status().details() << endl;
exit(1);
}

Data response_data = response.outputs(0).data();
cout << "Predicted concepts:" << endl;
for (int i = 0; i < response_data.concepts_size(); i++) {
const Concept& c = response_data.concepts(i);
cout << "\t" << c.name() << ": " << c.value() << endl;
}
var response = client.PostModelOutputs(
new PostModelOutputsRequest()
{
ModelId = "aaa03c23b3724a16a56b629203edc62c",
Inputs =
{
new List<Input>()
{
new Input()
{
Data = new Data()
{
Image = new Image()
{
Url = "https://samples.clarifai.com/dog2.jpeg"
}
}
}
}
}
},
metadata
);
let response = try client.postModelOutputs(
Clarifai_Api_PostModelOutputsRequest.with {
$0.modelID = "aaa03c23b3724a16a56b629203edc62c";
$0.inputs = [
Clarifai_Api_Input.with {
$0.data = Clarifai_Api_Data.with {
$0.image = Clarifai_Api_Image.with {
$0.url = "https://samples.clarifai.com/dog2.jpeg"
}
}
}
]
}
).response.wait()
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