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
We will be exploring two Azure Machine learning objects for working with data, in stores, and in sets. In Azure ML, Datastores references to storage locations, such as; Azure Storage blob containers, and every workspace has a default data store, usually Azure Storage Blob containers that was created with a workspace.
Now If you need to work with data that is stored in different locations you can add custom Datastores to your workspace and set any of them to be the default. To view the Datastores we've got, we can run the following code to see the Datastores we got available in our workspace. So to do so, or to get the default Datastore we can invoke default Data Store the store of our workspace objects. We can also enumerate all Datastores, indicating which is default.
So if you invoke DataStore's property of our workspace, You can get a sense of the Stores available. So we have a Workspaceblobstore, which is a default, we also have a Workspacefilestore. You can also view and manage Datastores in your workspace on the Datastores page for your workspace if you go into Azure ML studio,
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.