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Data Concepts in Azure Machine Learning

Data Concepts in Azure Machine Learning

Azure Machine Learning Data Concepts URI A Uniform Resource Identifier (URI) represents a storage location on a local computer, Azure storage, or a publicly available http(s) location. URIs can be used as inputs or outputs to an Azure Machine Learning job and can be mapped to the compute target filesystem in different modes: read-only mount, read-write mount, download, or upload. URIs use identity-based authentication to connect to storage services, with options for Azure Active Directory ID or Managed Identity. Data types Azure Machine Learning supports three data types: File, Folder, and Table. File: References a single file and can have [...]

Data Concepts in Azure Machine Learning2023-08-14T03:18:24+00:00

Azure Applied AI Services: Computer Vision and NLP Workloads on Azure

Computer Vision on Azure involves the use of Azure AI Services and related tools to analyze and understand visual content, such as images and videos. The goal is to enable computers to interpret and extract valuable information from visual data. Computer Vision Workloads on Azure: Azure AI Services Azure AI Services that analyzes and understands images and videos. It provides features like object detection, image recognition, image tagging, and OCR. Developers can extract insights from visual data and perform tasks like detecting objects and extracting text from images. Computer Vision simplifies the development of applications with advanced visual analysis capabilities [...]

Azure Applied AI Services: Computer Vision and NLP Workloads on Azure2023-09-20T10:59:15+00:00

Automated Machine Learning (AutoML) in Azure

Automated machine learning, known as automated ML or AutoML, streamlines the tasks involved in developing machine learning models by automating repetitive tasks. Azure Machine Learning provides the Python SDK, allowing you to leverage the power of AutoML. Applications of AutoML It simplifies the machine learning model development process, allowing users to implement ML solutions without extensive programming knowledge. Classification Identifies data points into categorical labels Used in scenarios like fraud detection, object detection, or handwriting recognition. Regression Analyzes relationships between continuous variables Predicts numerical outcomes Used in scenarios like predicting sales figures, estimating housing prices, or forecasting stock market trends. [...]

Automated Machine Learning (AutoML) in Azure2023-08-14T03:15:10+00:00

Azure Responsible AI

Azure Responsible AI Cheat Sheet Microsoft outlines six key principles for responsible AI: accountability, inclusiveness, reliability and safety, fairness, transparency, and privacy and security. Accountability People designing and deploying AI systems need to be accountable for their actions and decisions. Internal review bodies can provide oversight and guidance in AI systems. Inclusiveness AI should consider all human races and experiences. Inclusive design practices can address potential barriers. Assistive technologies should be used to empower people with impairments. Reliability and safety AI systems need to be reliable, safe, and resistant to manipulation. Rigorous testing, validation, monitoring, and model tracking processes are [...]

Azure Responsible AI2023-08-14T03:14:53+00:00

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