- Lab
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Libraries: If you want this lab, consider one of these libraries.
- AI
- Cloud
Run a Batch Inferencing Pipeline and Obtain Outputs
In this hands-on lab, you will become familiar with creating a batch inferencing pipeline and obtaining its outputs in Azure Machine Learning, using the Python SDK. You can compose the code from scratch or walk through the process using a prepared notebook.
Lab Info
Table of Contents
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Challenge
Create a Compute Resource and Access the Provided Jupyter Notebook
Utilize the provisioned workspace to create a compute instance. This will be used to power a Jupyter Notebook and execute your machine learning tasks. Choose the smallest-/least-expensive general purpose machine size from the recommended options. NOTE: This compute is used to run the notebook, another compute cluster, already provisioned for you, is used as the compute target in your inferencing pipeline. These are two different things.
If you plan to utilize the existing Jupyter Notebook with the data and steps required to create and run the experiment already preconfigured, clone the repo from GitHub.
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Challenge
Train and Register a Model
Train and register a model to use in your batch inferencing pipeline.
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Challenge
Simulate Sample Data
Generate random data to simulate the thousands of patient records that are uploaded each day.
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Challenge
Create and Run a Batch Inferencing Pipeline
Build the necessary steps to create a batch inferencing pipeline, and then execute it.
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Challenge
View the Results
Obtain the outputs produced by the experiment.
Real skill practice before real-world application
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