Get started: Install • Tutorial • Demo Video • Docs • Examples

Learn more: Website • FAQ • Blog • Subscribe • Twitter • Contact

Deploy, manage, and scale machine learning applications

Deploy machine learning applications without worrying about setting up infrastructure, managing dependencies, or orchestrating data pipelines.

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

How it works

1. Define your app: define your app using Python, TensorFlow, and PySpark.

2. $ cortex deploy: deploy end-to-end machine learning pipelines to AWS with one command.

3. Serve predictions: serve real time predictions via horizontally scalable JSON APIs.

End-to-end machine learning workflow

Data ingestion: connect to your data warehouse and ingest data.

- kind: environment
  name: dev
  data:
    type: csv
    path: s3a://my-bucket/data.csv
    schema: [@col1, @col2, ...]

Data validation: prevent data quality issues early.

- kind: raw_column
  name: col1
  type: INT_COLUMN
  min: 0
  max: 10

Data transformation: use custom Python and PySpark code to transform data.

- kind: transformed_column
  name: col1_normalized
  transformer_path: normalize.py  # Python / PySpark code
  input: @col1

Model training: train models with custom TensorFlow code.

- kind: model
  name: my_model
  estimator_path: dnn.py  # TensorFlow code
  target_column: @label_col
  input: [@col1_normalized, @col2_indexed, ...]
  hparams:
    hidden_units: [16, 8]
  training:
    batch_size: 32
    num_steps: 10000

Prediction serving: serve real time predictions via JSON APIs.

- kind: api
  name: my-api
  model: @my_model
  compute:
    replicas: 3

Deployment: Cortex deploys your pipeline on scalable cloud infrastructure.

$ cortex deploy
Ingesting data ...
Transforming data ...
Training models ...
Deploying API ...
Ready! https://abc.amazonaws.com/my-api

Key features

• Machine learning pipelines as code: Cortex applications are defined using a simple declarative syntax that enables flexibility and reusability.

• End-to-end machine learning workflow: Cortex spans the machine learning workflow from feature management to model training to prediction serving.

• TensorFlow and PySpark support: Cortex supports custom TensorFlow code for model training and custom PySpark code for data processing.

• Built for the cloud: Cortex can handle production workloads and can be deployed in any AWS account in minutes.