Effortless Model Training on Azure
Azure Machine Learning provides a cloud-based environment for training and deploying machine learning models. Leverage powerful compute resources, manage experiments, and streamline your model development lifecycle.
Key Training Features
Utilize a wide range of compute options, from CPU-based virtual machines to powerful GPU clusters and managed Kubernetes services, to accelerate your training tasks.
Automatically discover the best model and hyperparameters for your data. AutoML handles feature engineering, algorithm selection, and model tuning, saving you time and effort.
Log all your training runs, including metrics, parameters, and outputs. Visualize performance, compare experiments, and reproduce results with ease.
Integrate training seamlessly into your MLOps pipelines for continuous integration and continuous deployment (CI/CD) of your machine learning models.
Incorporate fairness, interpretability, and error analysis tools directly into your training process to build more trustworthy AI systems.
Getting Started with Training
Azure AI ML offers multiple ways to initiate model training:
- Azure Machine Learning Studio: A visual, no-code/low-code interface for building and training models.
- Azure Machine Learning SDK: Programmatically define, submit, and manage training jobs using Python.
- Azure CLI ML Extension: Manage your ML resources and orchestrate training jobs from the command line.