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An Azure resource called Azure Computer Vision provides training and forecasting capabilities. It does not let you examine and visualize datasets, nor does it let you author models.
A variety of prebuilt entities, including persons, locations, and organizations, can be used for entity identification in PDF documents thanks to the named entity recognition capability in text analytics.
You are attempting to construct a correlation between two characteristics (a dog's age and body fat %) and one label in the current situation, which serves as a model (the likelihood of that dog becoming ill).
The integration of chatbots, multilingual content, and confidence scoring are all supported by Azure Text Analytics. It can understand 120 different languages. documents must not exceed 5,120 characters in length.
Transparency makes it clear what AI solutions are used for, how they operate, and what their limitations are. Other ethical AI guidelines are intended to be applied to all AI solutions, despite their flaws.
Responsible AI systems should engage and empower everyone. Regardless of physical ability, gender, sexual orientation, race, or other variables ensuring inclusion, AI should help all facets of society.
Azure Custom Vision's object detection feature can recognize logos in photos. A set of photos is categorized into groups using Azure Custom Vision's image classification capabilities. For the purpose of recognizing faces, Azure Face Service is employed. The service is unable to recognize logos. LUIS is employed to comprehend natural language.
Analyzing historical data enables anomaly detection, which looks for odd occurrences like variations in engine speed or brake temperature. The situation referred to in the question does not involve conversational AI, computer vision, or natural language processing.
Convolutional neural networks (CNNs) are used in contemporary image classification approaches to discover patterns in the pixels that make up a picture and map them to a certain class. An artificial intelligence approach called anomaly detection looks for out-of-the-ordinary occurrences in data patterns. Instead of being classifications, both linear and multiple linear regression are regression procedures.
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The F1 score and accuracy measures can be used to assess classification methods. For classification evaluation, the F1 score combines precision and recall, whereas accuracy assesses the proportion of accurate predictions. Clustering model evaluation uses the Rand index. Regression model evaluation is done using MSE.
Algorithms are provided by the Speaker Recognition service to check and distinguish speakers based on their distinctive voice characteristics. To create transcripts of a discussion, use the Conversation transcription service. Evaluation of pronunciation measures the precision and fluidity of spoken audio. Because LUIS is used to interpret natural language, which cannot be utilized to distinguish speakers, it is a distraction.
The main distinction between multiple linear regression and linear regression, which employs a single feature, is that multiple linear regression models relationships between many features and a single label. A classification model is a logistic regression, and a clustering approach is a hierarchical clustering.
OCR enables the extraction of printed text. Because image classification cannot recognize words on an object, it is a distraction. The method of handling linguistic data known as "natural language processing" is distracting. Since it is used to divide up specific portions of an image, image segmentation is incorrect.
Activities are shared via many channels, including email, web chat, and Microsoft Teams. Cards are decorative objects that convey messages. A flow of actions creates dialog. Turn-by-turn completion of tasks results in a user contact with a chatbot.
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Machine learning tasks for categorization, regression, and forecasting are supported by AutoML UI. On the clustering and reinforcement learning are not supported.
You can choose to deploy a predictive service to Azure Container Instance (ACI) or Azure Kubernetes Service in Azure Machine Learning (AKS). Use an AKS deployment, which necessitates constructing an inference cluster compute target, for production scenarios. Testing can be done using deployments based on ACI. Predictive services cannot be deployed to Azure Functions or Azure Logic Apps using Azure Machine Learning.
