The Skymind platform guides engineers through the entire workflow of building and deploying ML models for enterprise applications on JVM infrastructure.
Discuss use case, available data, and desired outcome.
Deep dive into business requirements and deployment scenarios.
Skymind engineer to guide installation, setup, and configuration.
Seamlessly swap models from sandbox into production in a couple lines of code.
import skil_clientuploads = client.upload("tensorflow_rnn.pb") new_model = DeployModel(name="recommender_rnn", scale=30, file_location=uploads.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")
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