Download Microsoft Azure AI Fundamentals.AI-900.VCEplus.2020-09-08.36q.vcex

In Azure Machine Learning, the percentage split is the available technique to split the data. In this technique, random data of a given percentage will be split to train and test data.Reference: https://www.sqlshack.com/prediction-in-azure-machine-learning/

Model Explain Ability.Most businesses run on trust and being able to open the ML "black box" helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.Reference: https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/

Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.Reference:https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Reliability and safety: To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.Reference:https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.Reference:https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/

A: The trained model is stored as a Dataset module in the module palette. You can find it under My Datasets.Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.D: You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.Reference:https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

What does a good model look like?An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.Reference:https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification

You can drag-and-drop datasets and modules onto the canvas.Reference:https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer 

A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets. The studio currently supports training/validation data splitsReference:https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2