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Cortex deploys your machine learning models to your cloud infrastructure. You define deployments with simple declarative configuration and Cortex deploys your models as JSON APIs on your AWS account. It also handles autoscaling, rolling updates, log streaming, inference on CPUs or GPUs, and more.

Cortex is actively maintained by Cortex Labs. We're a venture-backed team of infrastructure engineers and we're hiring.

Define your deployment using declarative configuration:

# cortex.yaml

- kind: api
  name: my-api
  model: s3://my-bucket/my-model.zip
  request_handler: handler.py
  compute:
    min_replicas: 5
    max_replicas: 20

Customize request handling (optional):

# handler.py

def pre_inference(sample, metadata):
  # Python code


def post_inference(prediction, metadata):
  # Python code

Deploy to your cloud infrastructure:

$ cortex deploy

Deploying ...
Ready! https://amazonaws.com/my-api

Serve real time predictions via scalable JSON APIs:

$ curl -d '{"a": 1, "b": 2, "c": 3}' https://amazonaws.com/my-api

{ prediction: "def" }

• Minimal declarative configuration: Deployments can be defined in a single cortex.yaml file.

• Autoscaling: Cortex can automatically scale APIs to handle production workloads.

• Multi framework: Cortex supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more.

• Rolling updates: Cortex updates deployed APIs without any downtime.

• Log streaming: Cortex streams logs from your deployed models to your CLI.

• CPU / GPU support: Cortex can run inference on CPU or GPU infrastructure.