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Azure Machine Learning provides an environment to create and manage the end-to-end life cycle of Machine Learning models. Azure Machine Learning’s compatibility with open-source frameworks and platforms like PyTorch and TensorFlow makes it an effective all-in-one platform for integrating and handling data and models. Azure Machine Learning is designed for all skill levels, with advanced MLOps features and simple no-code model creation and deployment.

Getting started with Azure Machine Learning

Azure Machine Learning (Azure ML) is a cloud-based environment where you can build and manage machine learning models. It’s designed to govern the entire ML life cycle, so you can train and deploy models without focusing on setup. The platform is suitable for any kind of machine learning, from classical to deep learning, to supervised and unsupervised learning.

Azure ML is structured to help teams of data scientists and ML engineers make the most of their existing data processing and model development skills. Whether you prefer Python or R – or have previous experience with other open-source platforms such as PyTorch and TensorFlow – Azure ML is flexible enough to support these platforms and accelerate your work.

With built-in services like Azure ML studio that provide a user-friendly interface, and Automated Machine Learning capabilities that assist you in model selection and training, Azure ML has tools and features to suit every level of experience.

Kickstart your Azure Machine Learning journey

Whether you’re a developer or simply someone who wants to get a feel for what Azure Machine Learning is all about, there are plenty of learning resources out there.

You can also find further learning resources on the Azure ML learning resources page.

Try out Azure Machine Learning

One of the best ways to get grips with new tools and software is simply to give it a go. There’s no better way to do this than getting stuck into Azure Machine Learning itself.

Trying out Azure Machine Learning is free, so give it a whirl today.

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