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Registering a New Version of the Model & Cleaning Up

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Overview
DifficultyIntermediate
Duration1h 23m
Students58
Ratings
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Description

Learn how to operate machine learning solutions at cloud scale using the Azure Machine Learning SDK. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion, data preparation, model training, and model deployment in Microsoft Azure.

If you have any feedback related to this course, please contact us at support@cloudacademy.com.

Learning Objectives

  • Create an Azure Machine Learning workspace using the SDK
  • Run experiments and train models using the SDK
  • Optimize and manage models using the SDK
  • Deploy and consume models using the SDK

Intended Audience

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks, such as Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Prerequisites

  • Fundamental knowledge of Microsoft Azure
  • Experience writing Python code to work with data using libraries such as Numpy, Pandas, and Matplotlib
  • Understanding of data science, including how to prepare data and train machine learning models using common machine learning libraries, such as Scikit-Learn, PyTorch, or Tensorflow




Transcript

We've now trained a new model and we can go ahead and register it as a new version in the workspace. To do this, we import our model class. We invoke register model, from our run object. We specify the model path, the model name, some tags, so here, the training context is Parameterized SkLearn Estimator. 

The properties we wanna capture is area under the curve as well as accuracy information. We can also retrieve a listing of the models that we have in our workspace, so model, and then we pass on our workspace, so we get a listing of all the models we have in our workspace. And then we print that to the screen.

So, as you can see, the output you get is the version number, here is two. The training context, which we specified, which was Parameterized SkLearn Estimator. If you finish exploring your code we can free up resources by going to file, close, and halt.

About the Author
Students59
Courses1

Kofi is a digital technology specialist in a variety of business applications. He stays up to date on business trends and technology and is an early adopter of powerful and creative ideas.
His experience covers a wide range of topics including data science, machine learning, deep learning, reinforcement learning, DevOps, software engineering, cloud computing, business & technology strategy, design & delivery of flipped/social learning experiences, blended learning curriculum design and delivery, and training consultancy.