Compilación de soluciones de IA con Azure Machine Learning
Azure Machine Learning es una plataforma en la nube diseñada para entrenar, implementar, administrar y supervisar modelos de aprendizaje automático. Obtenga información sobre cómo usar el SDK de Python para Azure Machine Learning a fin de crear soluciones de IA para empresas.
Requisitos previos
En esta ruta de aprendizaje se da por hecho que tiene experiencia en el entrenamiento de modelos de aprendizaje automático con Python y marcos de código abierto como Scikit-Learn, PyTorch y Tensorflow. Si no es así, debe completar la ruta de aprendizaje Creación de modelos de aprendizaje automático antes de iniciar esta.
Módulos en esta ruta de aprendizaje
Obtenga información sobre cómo usar Azure Machine Learning para entrenar un modelo y registrarlo en un área de trabajo.
Data is the foundation of machine learning. In this module, you will learn how to work with datastores and datasets in Azure Machine Learning, enabling you to build scalable, cloud-based model training solutions.
One of the key benefits of the cloud is the ability to use scalable, on-demand compute resources for cost-effective processing of large data. In this module, you'll learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.
Orchestrating machine learning training with pipelines is a key element of DevOps for machine learning. In this module, you'll learn how to create, publish, and run pipelines to train models in Azure Machine Learning.
Machine learning models are often used to generate predictions from large numbers of observations in a batch process. To accomplish this, you can use Azure Machine Learning to publish a batch inference pipeline.
Choosing optimal hyperparameter values for model training can be difficult, and usually involved a great deal of trial and error. With Azure Machine Learning, you can leverage cloud-scale experiments to tune hyperparameters.
Aprenda a usar ML automatizado en Azure Machine Learning para encontrar el mejor modelo para los datos.
Data scientists have an ethical (and often legal) responsibility to protect sensitive data. Differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values.
Many decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions models make.
Machine learning models can often encapsulate unintentional bias that results in unfairness. With Fairlearn and Azure Machine Learning, you can detect and mitigate unfairness in your models.
After a machine learning model has been deployed into production, it's important to understand how it is being used by capturing and viewing telemetry.
