Machine learning

Machine learning is a subset of AI that allows machines to detect patterns and learn from data without expressly being programed for it. Azure Machine Learning solutions can advance your computing insights.

Azure empowers you with the most advanced machine learning capabilities. Quickly and easily build, train, and deploy your machine learning models by using Azure Machine Learning. Machine learning AI can be used for any kind of machine learning, from classical to deep, supervised, and unsupervised learning. Whether you prefer to write Python or R code, or use zero-code or low-code options such as the designer, you can build, train, and track highly accurate machine learning and deep learning models in a machine learning workspace.

You can even start training on your local machine and then scale out to the cloud. The service also interoperates with popular deep learning and reinforcement open-source tools such as PyTorch, TensorFlow, scikit-learn, and Ray and RLlib.

Get started with the Azure Machine Learning overview, where you'll find a tutorial on how to set up your first machine learning experiment. To learn more about the open-source model format and runtime for machine learning, see ONNX Runtime.

Common scenarios for machine learning solutions include:

• Predictive maintenance
• Inventory management
• Fraud detection
• Demand forecasting
• Intelligent recommendations
• Sales forecasting

Machine learning checklist

• Get started by first familiarizing yourself with machine learning, and then choose which experience to begin with. You can follow along with steps to use a Jupyter notebook with Python, the visual drag-and-drop experience, or automated machine learning (AutoML).

  • Learn about Azure Machine Learning
  • Create your first machine learning experiment with a Jupyter notebook with Python
  • Experiment with visual drag-and-drop
  • Use the AutoML UI
  • Configure your dev environment

• Experiment with more advanced tutorials to predict taxi fees, classify images, and build a pipeline for batch scoring.

  • Use AutoML to predict taxi fees
  • Classify images with scikit-learn
  • Build a machine learning pipeline for batch scoring

• Follow along with video tutorials to learn more about the benefits of machine learning, such as no-code model building, machine learning operations (MLOps), ONNX Runtime, model interpretability and transparency, and more.

  • What's new with Azure Machine Learning
  • Use AutoML to build models
  • Build zero-code models with Azure Machine Learning designer
  • MLOps for managing the end-to-end lifecycle
  • Incorporating ONNX Runtime into your models
  • Model interpretability and transparency
  • Building models with R

• Review reference architectures for AI machine learning solutions.

Next steps

Explore other AI solution categories:

• AI applications and agents
• Knowledge mining