Registering a New Version of the Model & Cleaning Up
Running an Experiment
Running a Training Script
Datastores & Datasets
Deploying the model
The course is part of this learning path
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 email@example.com.
- 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
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.
- 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
The GitHub repo for this course, containing the code and datasets used, can be found here: https://github.com/cloudacademy/using-the-azure-machine-learning-sdk
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.
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