Machine learning infrastructure for developers: build and deploy scalable TensorFlow applications on AWS 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.
- kind: raw_columnname: col1type: INT_COLUMNmin: 0max: 10
- kind: environmentname: devdata:type: csvpath: s3a://my-bucket/data.csvschema: [@col1, @col2, ...]
- kind: transformed_columnname: col1_normalizedtransformer_path: normalize.py # Python / PySpark codeinput: @col1
- kind: modelname: my_modelestimator_path: dnn.py # TensorFlow codetarget_column: @label_colinput: [@col1_normalized, @col2_indexed, ...]hparams:hidden_units: [16, 8]training:batch_size: 32num_steps: 10000
- kind: apiname: my-apimodel: @my_modelcompute:replicas: 3
$ cortex deployIngesting data ...Transforming data ...Training models ...Deploying API ...Ready! https://abc.amazonaws.com/my-api
End-to-end machine learning workflow: Cortex spans the machine learning workflow from feature management to model training to prediction serving.
Machine learning pipelines as code: Cortex applications are defined using a simple declarative syntax that enables flexibility and reusability.
Built for the cloud: Cortex can handle production workloads and can be deployed in any AWS account in minutes.