To parallelize the workload, you want to combine 10 invoices as a mini batch and send to one machine for execution. You have 10K invoices to be extracted using your form recognizer model. Let’s start with input data partitioning.
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You only need to provide full data inputs, scoring script, and necessary configures, the left will be taken care of. ParallelRunStep is a managed stack with out-of-the-box parallelism. Managed stack with out-of-the-box parallelism
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For example, how to partition the large amounts of input data? How to distribute and coordinate workloads across a cluster of machines? How to consolidate the output results? How to manage the machine cluster to avoid unnecessary cost? What if a task fails or machine dies? But it’s challenging to take advantage of scalable compute resource to parallelize the large workload to achieve this. Often times, data scientists and engineers want to generate many predictions at once. The predictions are stored and accessible for further usage. It is the process of generating predictions on a high volume of instances without the need of instant responses. ParallelRunStep provides parallelism out of the box and makes it extremely easy to scale fire-and-forget inference to large clusters of machines, thereby increasing development productivity and decreasing end-to-end cost.īatch inference is now being widely applied to businesses, whether to segment customers, forecast sales, predict customer behaviors, predict maintenance, or improve cyber security.
Today, we are announcing the general availability of Batch Inference in Azure Machine Learning service, a new solution called ParallelRunStep that allows customers to get inferences for terabytes of structured or unstructured data using the power of the cloud.
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