What if you could build, train, and deploy machine learning models without ever setting up a local environment? Azure ML offers three distinct paths to accelerate your workflow.
#1about 4 minutes
A quick refresher on AI, ML, and deep learning concepts
Learn the fundamental differences between AI, machine learning, and deep learning, along with the three main algorithm categories: supervised, unsupervised, and reinforcement.
#2about 4 minutes
Introducing the Azure Machine Learning platform and workspace
Get an overview of the Azure Machine Learning platform, its core components like the workspace backend, and its integration with other Azure services.
#3about 7 minutes
Setting up your Azure ML Studio and compute resources
Follow a step-by-step guide to creating an Azure ML workspace in the portal and configuring compute instances and clusters for model training.
#4about 6 minutes
Building models visually with the drag-and-drop designer
Discover how to create a complete machine learning pipeline using the visual designer to clean data, train a linear regression model, and evaluate its performance.
#5about 9 minutes
Using AutoML for automated model creation and selection
Explore how Automated ML (AutoML) automatically selects features, chooses the best algorithm, and tunes hyperparameters to build a high-performing classification model.
#6about 10 minutes
Developing models with a code-first approach using notebooks
Learn how to use the integrated Jupyter Notebook experience to prepare data, configure an AutoML run, and train a regression model using the Python SDK.
#7about 2 minutes
Understanding the ONNX format for model interoperability
Discover ONNX (Open Neural Network Exchange), a standard format that enables model portability and optimized performance across different platforms and devices.
#8about 9 minutes
Key takeaways and recommended learning resources
Review the main capabilities of Azure Machine Learning and find recommended links to Microsoft Learn tutorials and certifications to continue your journey.
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