Try the free or paid version of Azure Machine Learning.Īn Azure Machine Learning workspace. If you don't have an Azure subscription, create a free account before you begin. Prerequisitesīefore following the steps in this article, make sure you have the following prerequisites:Īn Azure subscription.
In the cloned repository, open the notebook: mnist-batch.ipynb. You can follow along this sample in the following notebooks. The files for this example are in: cd endpoints/batch/deploy-models/mnist-classifier Select the notebook you want to try out and click on Clone this notebook. Navigate to the folder SDK v2/sdk/python/endpoints/batch. On the left navigation bar, select the option Notebooks. To run the commands locally without having to copy/paste YAML and other files, first clone the repo and then change directories to the folder: The example in this article is based on code samples contained in the azureml-examples repository. In the second half, we're going to see how we can create a second deployment using a model created with TensorFlow (Keras), test it out, and then switch the endpoint to start using the new deployment as default. Such deployment will become our default one in the endpoint. In the first section of this tutorial, we're going to create a batch deployment with a model created using Torch. In this example, we're going to deploy a model to solve the classic MNIST ("Modified National Institute of Standards and Technology") digit recognition problem to perform batch inferencing over large amounts of data (image files). In this article, you'll learn how to use batch endpoints to deploy a machine learning model to perform inference. You can take advantage of parallelization.You don't have low latency requirements.You need to perform inference over large amounts of data, distributed in multiple files.You have expensive models that requires a longer time to run inference.
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