Azure Machine Learning service
Accelerate machine learning from the cloud to the edge
- Build and train machine learning models faster, and easily deploy to the cloud or the edge with Azure Machine Learning service.
- Use the latest open source technologies such as TensorFlow, PyTorch, or Jupyter.
- Experiment locally and then quickly scale up or out with large GPU-enabled clusters in the cloud.
- Speed up data science with automated machine learning and hyper-parameter tuning.
- Track your experiments, manage models, and easily deploy with integrated CI/CD tooling.
Why Azure Machine Learning service?
Increase your rate of experimentation and build models faster with automated machine learning and managed compute.
Use the machine learning libraries and IDEs that you already know.
Benefit from enterprise readiness with the security and compliance features of Azure and virtual network support.
Build, train, manage, and deploy machine learning models from the cloud to the edge.
What comes with Azure Machine Learning service
Automated machine learning and hyper-parameter tuning
Identify the best algorithms faster with automated machine learning, and find the best model efficiently with intelligent hyper-parameter tuning.
Version control and reproducibility
Increase your rate of experimentation by tracking and logging your experiments for reproducibility and easy modification.
Support for open source libraries and IDEs
Use machine learning libraries such as Tensorflow, PyTorch, and scikit-learn. Azure Machine Learning service integrates with your favorite Python IDE, including Visual Studio Code, Visual Studio, Azure Databricks notebooks, or Jupyter notebooks.
Proactively manage and monitor your models using the image and model registry, and upgrade them through integrated CI/CD.
Deploy models where you need them most with managed deployments to the cloud and the edge.
Distributed deep learning
Build better models faster with massive, managed GPU clusters. Train models quickly with distributed deep learning, and deploy them on FPGAs.
How to use Azure Machine Learning service
Step 1: Create a workspace
Install the SDK and create your workspace to store your compute resources, models, deployments, and run histories in the cloud.
Step 2: Train a model
Train a model locally or in the cloud, using open source machine learning libraries. Track your experiments and easily scale up or out your training with managed compute resources in the cloud.
Step 3: Deploy and manage
Deploy your model to test or production to generate predictions. Deploy to the cloud or at the edge, or leverage hardware-accelerated models on FPGAs for super-fast inferencing. When your model is in production, monitor it for performance and data drift, and retrain it as needed.