Simple Way to use Azure Machine Learning Designer

Introduction to ML

Machine Learning (ML) has revolutionized various industries by enabling computers to learn from data and make predictions or decisions. Azure Machine Learning (Azure ML) is a powerful cloud-based platform provided by Microsoft, designed to build, train, and deploy ML models efficiently. For those new to the world of ML, Azure ML Designer offers a user-friendly, visual interface to create ML workflows without writing a single line of code. This blog will explore Azure ML Designer and how it simplifies the ML development process.

Azure Machine Learning Environment

Azure Machine Learning provides a comprehensive environment for end-to-end ML development. It offers tools like Azure ML Designer, a visual interface for creating ML workflows without coding, and Azure ML Studio, a collaborative workspace for ML projects. Azure ML also provides Azure Data Factory for data preparation, Azure Databricks for big data analytics, and Azure Notebooks for interactive data exploration. Model training can be done using Azure’s powerful GPU-enabled virtual machines, and models can be deployed using Azure Kubernetes Service or Azure Functions. The platform’s scalability and integration with other Azure services make it a robust choice for building, training, and deploying ML models at scale.

What is Azure Machine Learning Designer?

Azure ML Designer is a graphical interface within the Azure ML platform that simplifies the creation of ML models. It allows users to construct ML pipelines by dragging and dropping pre-built modules onto a canvas and connecting them to define the workflow. The Designer abstracts complex ML operations, making it accessible to data scientists, developers, and domain experts with little or no coding knowledge.

Designer Benefits and Feasibility

• Ease of Use: Azure ML Designer eliminates the need for manual coding, reducing the learning curve and enabling users to focus on ML concepts and data insights.
• Visual Interface: The drag-and-drop interface makes it easy to design ML workflows, encouraging collaboration between team members with different skill sets.
• Pre-built Modules: Azure ML Designer offers a wide range of pre-built modules for data preprocessing, feature engineering, model training, evaluation, and deployment, saving time and effort.
• Scalability: Azure ML’s cloud-based infrastructure ensures that models can scale effortlessly to handle large datasets and complex ML tasks.

Key Steps for Beginners

1. Data Ingestion: Import your dataset into Azure ML Studio from various sources, such as local files, web files or open datasets. Your dataset should include historical features and known labels.

2. Selecting Columns: Before applying data preprocessing techniques drag a Select Columns in the Dataset module to the canvas, below the dataset module. Then connect the output at the bottom of the dataset module to the input at the top of the Select Columns in the Dataset module.

Select the Select Columns in the Dataset module, and in its Settings pane on the right, select Edit column. Then in the Select columns window, select By name and use the + links to add all columns other than normalized-losses.

3. Data Preprocessing: Use built-in modules to clean, transform, and preprocess the data to make it suitable for ML. You simply need to drag and drop these modules from the assets to the canvas and manipulate the parameters as per the requirement.

For example: Drag a Clean Missing Data module from the Data Transformations section, and place it under the Select Columns in Dataset module. Then connect the output from the Select Columns in Dataset module to the input of the Clean Missing Data module.

Select the Clean Missing Data module, and in the settings pane on the right, click the Edit column. Then in the Select columns window, select With rules, in the Include list select Column names.

4. Model Selection: Once you have the data, you can train machine learning model to predict. Choose the appropriate ML algorithm for your problem, such as regression, classification, or clustering.

5. Split Data: Drag a Split Data module onto the canvas under the Normalize Data module. Then connect the Transformed Dataset (left) output of the Cleaned Data or Normalize Data module to the input of the Split Data module. Configure various settings for split data modules like Splitting mode, Fraction of rows in the first output dataset, and Random seed.

You can even add comments to perform a particular functionality.

6. Model Training: Expand the Model Training section in the pane on the left, and drag a Train Model module to the canvas, under the Split Data module. Then connect the output of the Split Data module to the Dataset (right) input of the Train Model module as shown below:

7. Score Model: To test the trained model, we need to use it to score the validation dataset we held back when we split the original data — in other words, predict labels for the features in the validation dataset. Expand the Model Scoring & Evaluation section and drag a Score Model module to the canvas, below the Train Model module. Then connect the output of the Train Model module to the Trained model (left) input of the Score Model module; and drag the Results dataset2 (right) output of the Split Data module to the Dataset (right) input of the Score Model module.

8. Model Evaluation: Use evaluation modules to assess the model’s performance using metrics like accuracy, precision, recall, etc.

9. Submit the pipeline: Click on the submit button to submit your pipeline if you are satisfied with your model evaluation.

10. Model Deployment: Once satisfied with the model’s performance, deploy it as a web service for real-time predictions. 🎉

Conclusion

Azure Machine Learning Designer is an excellent tool for beginners to kickstart their ML journey without the need for extensive programming knowledge. Its user-friendly interface, pre-built modules, scalability, and automated capabilities make it a valuable asset for data scientists and developers looking to build and deploy ML models efficiently on the Azure platform. So why wait? Dive into the world of Azure ML Designer and unleash the power of machine learning!