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- Automated Artificial Intelligence (AutoAI) is a variation of the automated machine learning, or AutoML, technology, which extends the automation of model building towards automation of the full life cycle of a machine learning model. It applies intelligent automation to the task of building predictive machine learning models by preparing data for training, identifying the best type of model for the given data, then choosing the features, or columns of data, that best support the problem the model is solving. Finally, automation tests a variety of tuning options to reach the best result as it generates, then ranks, model-candidate pipelines. The best performing pipelines can be put into production to process new data, and deliver predictions based on the model training. Automated artificial intelligence can also be applied to making sure the model does not have inherent bias and automating the tasks for continuous improvement of the model. Managing an AutoAI model requires frequent monitoring and updating, managed by a process known as model operations, or ModelOps. The Automated Machine Learning and Data Science Team (AMLDS), a small team within IBM Research, which was formed to “apply techniques from Artificial Intelligence (AI), Machine Learning (ML), and data management to accelerate and optimize the creation of machine learning and data science workflows,” is credited with advancing the development of AutoAI. (en)
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| rdfs:comment
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- Automated Artificial Intelligence (AutoAI) is a variation of the automated machine learning, or AutoML, technology, which extends the automation of model building towards automation of the full life cycle of a machine learning model. It applies intelligent automation to the task of building predictive machine learning models by preparing data for training, identifying the best type of model for the given data, then choosing the features, or columns of data, that best support the problem the model is solving. Finally, automation tests a variety of tuning options to reach the best result as it generates, then ranks, model-candidate pipelines. The best performing pipelines can be put into production to process new data, and deliver predictions based on the model training. Automated artificial (en)
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