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Whether you're training a machine learning scikit-learn model from the ground-up or you're bringing an existing model into the cloud, you can use Azure Machine Learning to scale out open-source training jobs using elastic cloud compute resources. You can build, deploy, version, and monitor ...
orient_theta—Sets the width of orientation mask. The default is 8. mtl_model—Sets the architecture type that will be used to create the model. Valid choices are linknet or hourglass for linknet-based or hourglass-based, respectively, neural architectures. The default is hourglass. monitor—Spe...
This section sets up the job for training by loading the required Python packages, connecting to a workspace, creating a compute resource to run a command job, and creating an environment to run the job. Connect to the workspace First, you need to connect to your Azure Machine Learning works...
sizes, from small desk ornaments to larger tabletop arrangements. The set's compatibility with other model train sets makes it a valuable addition to any collection. Whether you're looking to add to your existing train set or searching for the best place to sell model train, this set is ...
In this article, learn how to run your TensorFlow training scripts at scale using Azure Machine Learning Python SDK v2. The example code in this article train a TensorFlow model to classify handwritten digits, using a deep neural network (DNN); register the model; and deploy it to an online...