Introduction
Running an Experiment
Running a Training Script
Datastores & Datasets
Compute
Pipelines
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 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
Resources
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
I hope you remember that the outputs of the experiment also includes the Training model. Well, we can register this model in our Azure Machine Learning workspace, making it possible to track model versions and retrieve them later.
So let's go ahead and register a model. We need to import our Model class and to register it we use our run object, invoked register_model, specify the model_path, and then the model_name, okay.
We can also add additional target information. So the Training context, here is the Estimator and additional properties we have AUC, which we can get from run object. So if we go get_metrics, we get that information put through. We can also get the Accuracy information by invoking get_metrics as well as specify the Accuracy.
So here we can list registered models by getting all of the models in our workspace and onto our screen. So we already have one version over here and the additional tag information that was specified. We get to see that an output. So the training context, is estimator. We also specified AUC a property, and then accuracy as well.
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.