Automating the construction and tuning of machine learning (ML) models has long been one of the goals of the ML community. This is due to several factors, most notably a sharp increase in the demand for tailored AI solutions, a relative scarcity of trained ML scientists, and the development of deep learning models with complex architectures requiring accurate design and fine-tuning.
Existing automated machine learning (AutoML) techniques have been remarkably successful in identifying good parameters for a given model, sometimes even outperforming humans. However, these options either take too long to train or they work for only a handful of parameters. That’s why Azure Machine Learning uses probabilistic latent variable model to work with DNNs without needing to fully train them. Azure Machine Learning Services (AML) provides a cloud-based environment you can use to develop, train, test, deploy, manage, and track machine learning models. AutoML in the cloud will soon become mainstream.