Microsoft Azure AI Fundamentals: Get started with artificial intelligence Flashcards
What is AI?
AI is the creation of software that imitates human behaviors and capabilities.
AI Key workloads
Machine learning
Anomaly detection
Computer vision
Natural language processing
Knowledge mining
Machine learning
This is often the foundation for an AI system, and is the way we “teach” a computer model to make prediction and draw conclusions from data.
Anomaly detection
The capability to automatically detect errors or unusual activity in a system.
Computer vision
The capability of software to interpret the world visually through cameras, video, and images.
Computer vision is one of the core areas of artificial intelligence (AI), and focuses on creating solutions that enable AI applications to “see” the world and make sense of it.
Natural language processing
The capability for a computer to interpret written or spoken language, and respond in kind.
Knowledge mining
Knowledge mining
Understand machine learning
Machine Learning is the foundation for most AI solutions.
example :
Sustainable farming techniques are essential to maximize food production while protecting a fragile environment. The Yield, an agricultural technology company based in Australia, uses sensors, data and machine learning to help farmers make informed decisions related to weather, soil and plant conditions.
Machine learning in Microsoft Azure
Microsoft Azure provides the Azure Machine Learning service - a cloud-based platform for creating, managing, and publishing machine learning models. Azure Machine Learning provides the following features and capabilities:
Azure Machine Learning features and their Capabilities:
Automated machine learning:
This feature enables non-experts to quickly create an effective machine learning model from data.
Azure Machine Learning designer:
A graphical interface enabling no-code development of machine learning solutions.
Data and compute management:
Cloud-based data storage and compute resources that professional data scientists can use to run data experiment code at scale.
Pipelines:
Data scientists, software engineers, and IT operations professionals can define pipelines to orchestrate model training, deployment, and management tasks.
Anomaly detection in Microsoft Azure
In Microsoft Azure, the Anomaly Detector service provides an application programming interface (API) that developers can use to create anomaly detection solutions.
Computer Vision models and capabilities
Image classification
Image classification involves training a machine learning model to classify images based on their contents. For example, in a traffic monitoring solution you might use an image classification model to classify images based on the type of vehicle they contain, such as taxis, buses, cyclists, and so on.
Object detection:
Object detection machine learning models are trained to classify individual objects within an image, and identify their location with a bounding box. For example, a traffic monitoring solution might use object detection to identify the location of different classes of vehicle.
Semantic segmentation:
Semantic segmentation is an advanced machine learning technique in which individual pixels in the image are classified according to the object to which they belong. For example, a traffic monitoring solution might overlay traffic images with “mask” layers to highlight different vehicles using specific colors.
Image analysis:
You can create solutions that combine machine learning models with advanced image analysis techniques to extract information from images, including “tags” that could help catalog the image or even descriptive captions that summarize the scene shown in the image.
Face detection, analysis, and recognition:
Face detection is a specialized form of object detection that locates human faces in an image. This can be combined with classification and facial geometry analysis techniques to recognize individuals based on their facial features.
Optical character recognition (OCR):
Optical character recognition is a technique used to detect and read text in images. You can use OCR to read text in photographs (for example, road signs or store fronts) or to extract information from scanned documents such as letters, invoices, or forms.
