Getting started with AI on Azure
Azure AI Services for beginners.
AI is a design that mimics human behavior and abilities and is embedded into the software.
There some key elements include in this process.
- Machine learning — This is how we “train” a computer model to generate predictions and draw conclusions from data, and it is often the foundation for an AI system.
- Anomaly detection — The capability to automatically detect errors or unusual activity in a system.
- Computer vision — Software’s ability to visually perceive the world using cameras, video, and photos.
- Natural language processing — The ability of a computer to understand and respond to written or spoken language.
- Conversational AI — The capability of a software “agent” to participate in a conversation.
Machine learning
Machine Learning is the foundation for most AI solutions.
So how do machines learn?
The answer is data-driven. As we go about our daily lives in today’s society, we generate massive amounts of data. We generate vast amounts of data, from the text messages, emails, and social media postings we send to the images and movies we snap on our phones.
Millions of sensors in our homes, cars, communities, public transportation networks, and factories continue to generate more data. Data scientists can use all of that data to train machine learning models that can make predictions and inferences based on the relationships they find in the data.
Microsoft Azure Machine Learning Features.
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:
- 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
A machine learning-based technique that analyzes data over time and identifies unusual changes.
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.
Easily incorporate time-series anomaly detection tools into your apps to assist users in rapidly identifying concerns. Anomaly Detector ingests all types of time-series data and selects the most accurate anomaly-detection algorithm for your data. Using both univariate and multivariate APIs, detect spikes, troughs, and departures from cyclic patterns, as well as trend changes.
Computer Vision
Computer Vision is an area of AI that deals with visual processing.
Computer vision is a branch of artificial intelligence (AI) in which software systems are designed to visually perceive the world using cameras, pictures, and video. AI engineers and data scientists can handle a variety of computer vision problems using a combination of custom machine learning models and platform-as-a-service (PaaS) technologies.
Computer vision services in Microsoft Azure.
- Computer Vision — You can use this service to analyze images and video, and extract descriptions, tags, objects, and text.
- Custom Vision — Use this service to train custom image classification and object detection models using your own images.
- Face — The Face service enables you to build face detection and facial recognition solutions.
- Form Recognizer — Use this service to extract information from scanned forms and invoices.
Natural language processing
Natural language processing (NLP) is the area of AI that deals with creating software that understands written and spoken language.
Natural language processing in Microsoft Azure
- Text Analytics — Use this service to analyze text documents and extract key phrases, detect entities (such as places, dates, and people), and evaluate sentiment (how positive or negative a document is).
- Translator Text — Use this service to translate text between more than 60 languages.
- Speech — Use this service to recognize and synthesize speech, and to translate spoken languages.
- Language Understanding Intelligent Service (LUIS) — Use this service to train a language model that can understand spoken or text-based commands.
Conversational AI
Conversational AI is a phrase used to describe solutions in which AI agents engage in human-like discussions. Bots are often used in conversational AI solutions to manage user dialogs. Web site interfaces, email, social media platforms, message systems, phone calls, and other methods can all be used to have these conversations.
Conversational AI in Microsoft Azure
- QnA Maker — This cognitive service enables you to quickly build a knowledge base of questions and answers that can form the basis of a dialog between a human and an AI agent.
- Azure Bot Service — This service provides a platform for creating, publishing, and managing bots. Developers can use the Bot Framework to create a bot and manage it with Azure Bot Service — integrating back-end services like QnA Maker and LUIS, and connecting to channels for web chat, email, Microsoft Teams, and others.
Summary
Artificial Intelligence allows for the development of powerful solutions to a wide range of issues. AI systems can assess the environment around them, make predictions or conclusions, and act on them in ways that we couldn’t have imagined just a few years ago.
Thanks for reading!
