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Muhammad Abdur Rofi
Muhammad Abdur Rofi

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Introduction AI with Azure

What is AI?

Simply put, AI is the creation of software that imitates human behaviors and capabilities. Key elements include:

  • 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.
  • Natural language processing - The capability for a computer to interpret written or spoken language, and respond in kind.
  • Conversational AI - The capability of a software "agent" to participate in a conversation.

Understand machine learning

Machine Learning is the foundation for most AI solutions.

Let's start by looking at a real-world example of how machine learning can be used to solve a difficult problem.

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.

How machine learning works

So how do machines learn?

The answer is, from data. In today's world, we create huge volumes of data as we go about our everyday lives. From the text messages, emails, and social media posts we send to the photographs and videos we take on our phones, we generate massive amounts of information. More data still is created by millions of sensors in our homes, cars, cities, public transport infrastructure, and factories.

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.

For example, suppose an environmental conservation organization wants volunteers to identify and catalog different species of wildflower using a phone app. The following animation shows how machine learning can be used to enable this scenario.


  1. A team of botanists and data scientists collects samples of wildflowers.
  2. The team labels the samples with the correct species.
  3. The labeled data is processed using an algorithm that finds relationships between the features of the samples and the labeled species.
  4. The results of the algorithm are encapsulated in a model.
  5. When new samples are found by volunteers, the model can identify the correct species label.

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:

Feature Capability
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.

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