2 mins to read Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Learn how to use Azure Machine Learning to create and publish models without writing code.
Azure Machine Learning Studio (Azure ML Studio):
This is a drag-and-drop, no-code/low-code platform that allows you to create, experiment with, and manage machine learning solutions. Here’s how you can explore visual tools for machine learning on Azure:
- Data Preparation: Use Azure ML Studio’s visual tools to import, clean, and transform your data. You can connect to various data sources and perform data wrangling tasks visually.
- Model Building: Design machine learning models using a wide range of pre-built algorithms and modules available in Azure ML Studio. You can create a model pipeline by dragging and connecting different components, making it easy to experiment and iterate.
- Experimentation: Azure ML Studio provides tools to run experiments and track their performance. You can compare multiple models to determine which one works best for your specific problem.
- Hyperparameter Tuning: Optimize your models using hyperparameter tuning tools within Azure ML Studio. These visual tools help you find the best set of hyperparameters to improve model accuracy.
- Deployment: Once you have a trained model, you can easily deploy it as a web service directly from Azure ML Studio. This makes it accessible for real-time predictions and integration into other applications.
- Monitoring and Management: You can use visual tools to monitor the performance of your deployed models and manage them efficiently, ensuring they continue to deliver accurate results over time.