AI-900: What is Automated Machine Learning in Azure and How Does it Work?

Learn about Automated Machine Learning in Azure AI and how it enables you to automatically train models by specifying a dataset and target label to predict.

Question

Automated Machine Learning enabled you to specify a dataset and will automatically understand which label to predict.

A. True
B. False.

Answer

A. True

Explanation

Automated Machine Learning, also known as AutoML, is a feature in Azure Machine Learning that enables you to automatically build, train, and tune machine learning models. With Automated ML, you simply provide a labeled dataset and specify the label (target) you want to predict. Azure’s AutoML will then automatically:

1. Preprocess and clean the data
2. Select the most appropriate machine learning algorithm
3. Optimize model hyperparameters using techniques like grid search
4. Train multiple models and evaluate their performance
5. Select the best performing model

The key advantage of Automated Machine Learning is that it eliminates the need for you to manually experiment with different algorithms and hyperparameter settings to find the optimal model. By automating the model building process end-to-end, it enables both professional data scientists and domain experts with limited machine learning experience to quickly build high quality models.

When you provide a labeled dataset to AutoML, it will infer the type of label (regression for continuous values, classification for discrete categories, etc.) based on the data type of the label column. It then automatically selects an appropriate suite of algorithms to try.

In summary, Automated Machine Learning in Azure significantly simplifies and accelerates the development of machine learning models by automating the end-to-end workflow, including understanding the label to predict from the provided dataset. This enables faster experimentation and empowers a wider range of users to leverage machine learning.

Automated Machine Learning (AutoML) can analyze the specified dataset and automatically identify the target label to predict based on the dataset’s structure and contents. This streamlines the model-building process by reducing the need for manual specification of the target variable.

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