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Azure Labs
Labs badge iconLAB

Create a Real-time Inferencing Service with Azure ML

In this lab, you’ll create a real-time inference service with Azure ML. When you’re finished with this lab, you’ll have experience creating a compute instance, training, deploying and testing real-time endpoints using Azure Machine Learning SDK.

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Labs - Create a Real-time Inferencing Service with Azure ML

Lab info

Rating (28)
Level
Beginner
Duration
50m
Released
Jan 31, 2022

Lab author

Lab Author: Bismark Adomako
Bismark Adomako
Bismark believes that education is no longer a one-time investment, but instead a lifelong pursuit and that great authors and mentors can play an invaluable role in finding and developing a fulfilling career. He holds a Bachelor of Science degree in Computer Engineering specializing in Software Engineering, Artificial Intelligence and Distributed computing. He taught courses in robotics and computer vision as a student leader whiles in school, and interned and consulted for the British Counci... morel as a Software Engineer and Project Assistant. He currently works as a Business Intelligence and Big Data developer doubling up as a Data Scientist at Ecobank eProcess International S.A. developing machine learning models some of which includes the Customer360 model consisting of a customer segmentation model, customer churn model, and customer cross selling (product recommendation) model. He has enterprise level knowledge in data integration for BI & Data Management domain applications.
  • Table of contents
  • What's a lab?
Challenge

Train and Register a Machine Learning Model

You will create a compute instance to run your Jupyter notebooks on an Azure machine learning workspace, then clone a GitHub repository to aid you train and register a machine learning model to predict whether a flight will be delayed or not.

Challenge

Create an Entry Script and Execution Environment

You will learn to set up inference configuration, which includes the scoring and environment files required to load and use the registered model.

Challenge

Deploy Model to a Web Service Hosted on Azure Container Instance (ACI)

You will deploy the registered model as a web service so that an application can simply make an HTTP request, sending the flight data in JSON (or binary) format, and receive back the predicted class(es).

Challenge

Consume the Real-time Endpoint

You will learn to consume the deployed Web Service by call sending requests to the endpoint to make real-time predictions.

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Provided environment for hands-on practice

We will provide the credentials and environment necessary for you to practice right within your browser.

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Guided walkthrough

Follow along with the author’s guided walkthrough and build something new in your provided environment!

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Did you know?

On average, you retain 75% more of your learning if you get time for practice.

Start learning by doing today

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