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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.
Table of Contents
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.
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.
Train and Register a Model
Train and register a model to use in your batch inferencing pipeline.
Simulate Sample Data
Generate random data to simulate the thousands of patient records that are uploaded each day.
Create and Run a Batch Inferencing Pipeline
Build the necessary steps to create a batch inferencing pipeline, and then execute it.
View the Results
Obtain the outputs produced by the experiment.
What's a lab?
Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.