Azure Machine Learning service
Build, train, and deploy models from the cloud to the edge
Announcement
- Azure Machine Learning service is now generally available
- Read the blog
Overview
Simplify and accelerate the building, training, and deployment of your machine learning models. Use automated machine learning to identify suitable algorithms and tune hyperparameters faster. Improve productivity and reduce costs with autoscaling compute and DevOps for machine learning. Seamlessly deploy to the cloud and the edge with one click. Access all these capabilities from your favorite Python environment using the latest open-source frameworks, such as PyTorch, TensorFlow, and scikit-learn.
Why Azure Machine Learning service?
Productive
Build and train models faster with automated machine learning, autoscaling cloud compute, and built-in DevOps.
Open
Use Azure Machine Learning service from any Python environment and with your favorite frameworks and tools.
Trusted
Improve enterprise readiness with Azure security, compliance features, and virtual network support.
Hybrid
Build, train, and deploy your models on-premises, in the cloud, and on the edge.
Azure Machine Learning service capabilities
Automated machine learning
Identify suitable algorithms and hyperparameters faster.
Managed compute
Train models with ease and reduce costs by autoscaling powerful GPU clusters.
DevOps for machine learning
Increase productivity with experiment tracking, model management and monitoring, integrated CI/CD, and machine learning pipelines.
Simple deployment
Deploy models on-premises, to the cloud, and at the edge with a few lines of code.
Tool-agnostic Python SDK
Azure Machine Learning service integrates with any Python environment, including Visual Studio Code, Jupyter notebooks, and PyCharm.
Support for open-source frameworks
Use your favorite machine learning frameworks and tools, such as PyTorch, TensorFlow, and scikit-learn.
How to use Azure Machine Learning service
Step 1: Create a workspace
Install the SDK in your favorite Python environment, and create your workspace to store your compute resources, models, deployments, and run histories in the cloud.
Step 2: Build and train
Use frameworks of your choice and automated machine learning capabilities to identify suitable algorithms and hyperparameters faster. Track your experiments and easily access powerful GPUs in the cloud.
Step 3: Deploy and manage
Deploy models to the cloud or at the edge and leverage hardware-accelerated models on field-programmable gate arrays (FPGAs) for super-fast inferencing. When your model is in production, monitor it for performance and data drift, and retrain it as needed.
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