SKIL for DevOps and SRE Teams | Skymind

SKIL for DevOps and SRE Teams

Design, Build, and Maintain Efficient Large-Scale AI Workflows

Large-Scale, Distributed, Fault-Tolerant Machine Learning

SKIL ensures internal and external services have reliability and uptime appropriate to users' with an eye towards capacity and performance.

Command Line Interface

Set up internal services and monitor workflows from the command line.

          import skil_client
          uploads = client.upload("tensorflow_rnn.pb")
          new_model = DeployModel(name="recommender_rnn", scale=30, file_location=uploads[0].path)
          model = client.deploy_model(deployment_id, new_model)
          ndarray = INDArray(array=base64.b64encode(x_in))
          input = Prediction(id=1234, prediction=ndarray, needsPreProcessing=false)
          result = client.predict(input, "production", "recommender_rnn")
        

Standardization

  • Enforce a standard set of dependencies across Data Science and IT teams.
  • Managed AI layer backed by Skymind.

Model Management

  • Build, train, and evaluate models all within a single, easy-to-use interface
  • Single, collaborative workspace with cloning & version control

Embeddability

  • SKIL can be deployed within on-prem, cloud, or hybrid environments.
  • Run SKIL for continuous delivery and deployment as a microservice.

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