Microsoft AI School - No Code AI Tools

Microsoft AI School - Using Azure AI No-Code Visual Tools To Kickstart Your Learning

Introduction to Azure Machine Learning and Azure AI No-Code Visual Tools

Azure Machine Learning is a cloud-based platform that includes a wide range of features and capabilities for data scientists and engineers to prepare data, train models, publish predictive services, and monitor their usage in Azure. Not only can you create a model from scratch in Azure Machine Learning but also use models built from open-source platforms like TensorFlow, Pytorch, or scikit-learn. 

Microsoft Azure features no-code visual tools that allow you to deploy and deliver predictive models at a remarkable pace. It equips data scientists and ML engineers with tools that enable them to automate and accelerate their day-to-day workflows - Train, Deploy and Manage MLOps.The tools automatically detect errors and inconsistencies in data and rectify them. The best part here is that you can use these tools even without profound technical know-how.

Note: Azure Machine Learning designer is an interface that incorporates drag-and-drop features used to train and deploy models in the Azure Machine Learning platform.

Learn Azure Machine Learning Using Azure AI No-Code Visual Tools

Machine learning lies at the core of artificial intelligence, and it is often the foundation of most AI systems. You can learn the nuts and bolts of Azure Machine Learning to create and publish models without writing a single line of code with the help of Microsoft AI School’s learning path  - “Use visual tools to create machine learning models with Azure Machine Learning course.” This learning path has four modules.

Read ahead to get a quick peek at each module of this learning path. 

Prerequisite: You must be able to navigate the Azure portal to pursue this learning path.

Module 1: Use Automated Machine Learning in Azure Machine Learning

Automated machine learning, also known as AutoML, allows you to automate the iterative process of training a machine learning model that often requires a lot of time and compute resources. It allows data scientists, engineers, and analysts to build ML models with high efficiency, scalability, and productivity.

The objective of the first module of this learning path is to guide you to use the automated machine learning interface in Azure Machine Learning. After completing this module, you will have a clear picture of how to: 

• Identify the different types of machine learning models.
• Use AutoML capabilities of the Azure Machine Learning platform to train a predictive model and deploy it. 

Here’s an overview of what you will learn in this module:

Module 2: Create a Regression Model with Azure Machine Learning Designer

Regression is a supervised machine learning technique that is used to predict numeric values/labels.

The objective of this module is to introduce you to the Microsoft Machine Learning designer to train a regression model by simply using a drag and drop visual interface without the requirement of any code. Thus, after completing this module, you will have a clear picture of how to:

• Use the Azure Machine Learning designer and train a regression model.
• Use the regression model for inferencing after training it.
• Deploy the regression model as a predictive service using Azure Machine Learning.

Here’s an overview of what you will learn in this module:

Module 3: Create a Classification Model with Azure Machine Learning Designer

Classification is a machine learning technique used to predict which class or category an item belongs to after training the model using data that includes features and known values. After training, the model learns to fit feature combinations to the label. 

The objective of this module is to guide you to use the Azure Machine Learning designer to train a classification model, use it for interfacing and then deploy the model as a service. 

Here’s an overview of what you will learn in this module:

Module 4: Create a Clustering Model with Azure Machine Learning Designer

Clustering is an unsupervised machine learning technique that is used to group similar items or entities based on their features. In other words, clustering allows you to train a model that can separate the items into different clusters based on their characteristics/features. Unlike classification, in clustering, there are no known cluster values/labels from which you can train the model.

In this module, you will learn how to:

• Use the Azure Machine Learning designer interface and train a clustering model.
• Use the clustering model for inferencing after raining it.
• Finally, deploy the clustering model as a service using Azure Machine Learning.

Once again, you are not required to write a single line of code in order to build the model. You can create the entire model and deploy it as a service simply by using the drag and drop visual interface of the Azure Machine Learning designer. 

Here’s an overview of what you will learn in this module:

Conclusion

Thus, if you are beginning to learn Machine Learning and you have very little to no coding experience, then this course will definitely get you going from scratch. It will introduce you to a wide range of features and tools in Azure that can be used to launch your first machine learning model without the requirement of any coding experience.