Welcome to this course on Azure AI Fundamentals! This course is designed for anyone who wants to learn about artificial intelligence (AI) and the services in Microsoft Azure that you can use to build AI solutions. The course provides a practical, hands-on approach in which you will get a chance to see AI in action and try Azure AI services for yourself.

Audience

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services. This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial. Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it is not a prerequisite for any of them.

AI-900 Certification Exam

The exam includes five study areas. The percentages indicate the relative weight of each area on the exam. The higher the percentage, the more questions the exam will contain.

  • Describe AI workloads and considerations (15-20%)
  • Describe fundamental principles of machine learning on Azure (30-35%)
  • Describe features of computer vision workloads on Azure (15-20%)
  • Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
  • Describe features of conversational AI workloads on Azure (15-20%)

You can access the free on-demand training for A1-900 below:

Pre-requisites:

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

Module 1: Introduction to AI

In this module, you’ll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You’ll then explore considerations and principles for responsible AI development.

Artificial Intelligence in Azure

AI enables us to build amazing software that can improve health care, enable people to overcome physical disadvantages, empower smart infrastructure, create incredible entertainment experiences, and even save the planet! Simply put, AI is the creation of software that imitates human behaviors and capabilities.

Responsible AI

Artificial Intelligence is a powerful tool that can be used to greatly benefit the world. However, like any tool, it must be used responsibly. At Microsoft, AI software development is guided by a set of six principles, designed to ensure that AI applications provide amazing solutions to difficult problems without any unintended negative consequences.

Module 2 Machine Learning

In this module, you’ll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models.

Introduction to Machine Learning

Machine Learning is the foundation for most AI solutions, and enables the creation of models that predict unknown values and infer insights from observed 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.

Azure Machine Learning

Rapidly build and deploy machine learning models using tools that meet your needs regardless of skill level. Use the no-code designer to get started with visual machine learning or built-in collaborative Jupyter notebooks for a code-first experience. Accelerate model creation with the automated machine learning and access built-in feature engineering, algorithm selection and hyperparameter sweeping to develop highly accurate models.

MLOps or DevOps for machine learning, streamlines the machine learning lifecycle, from building models to deployment and management. Use ML pipelines to build repeatable workflows and use a rich model registry to track your assets. Manage production workflows at scale using advanced alerts and machine learning automation capabilities. Profile, validate and deploy machine learning models anywhere, from the cloud to the edge, to manage production ML workflows at scale in an enterprise ready fashion.

Access state-of-the-art responsible ML capabilities to understand protect and control your data, models and processes. Explain model behavior during training and inferencing and build for fairness by detecting and mitigating model bias. Preserve data privacy throughout the machine learning lifecycle with differential privacy techniques and use confidential computing to secure ML assets. Automatically maintain audit trails, track lineage and use model datasheets to enable accountability.

Module 3: Computer Vision

Computer vision is a the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you’ll explore multiple computer vision techniques and services.

Computer Vision Concepts

Computer vision is one of the core areas of artificial intelligence (AI), and focuses on creating solutions that enable AI-enabled applications to “see” the world and make sense of it. Of course, computers don’t have biological eyes that work the way ours do, but they are capable of processing images; either from a live camera feed or from digital photographs or videos. This ability to process images is the key to creating software that can emulate human visual perception.

Computer Vision in Azure

Azure provides a set of cognitive services that encapsulate common AI workloads – including computer vision tasks. To use cognitive services, you must provision a resource in your Azure subscription. This can be a standalone, service-specific resource (for example, a Computer Vision resource) or a general Cognitive Services resource that encapsulates multiple services. Using a standalone service-specific resource enables you to manage costs and access to that service independently of other services you may be using, while a general Cognitive Services resource enables you to combine all of your AI services in a single Azure resource for centralized management.

Module 4 Natural Language Processing (NLP)

This module describes scenarios for AI solutions that can process written and spoken language. You’ll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands.

Introduction to Natural Language Processing

Natural language processing (NLP) is the area of AI that deals with creating software that understands written and spoken language. NLP enables you to create software that can Analyze text documents to extract key phrases and recognize entities (such as places, dates, or people). In Microsoft Azure, you can use the following cognitive services to build natural language processing solutions like Text Analytics, Translator Text, Speech.

Using NLP Services

The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text. You can use the language detection capability of the Text Analytics service to identify the language in which text is written. You can submit multiple documents at a time for analysis.

Module 5 Conversational AI

This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions.

Conversational AI Concepts

Increasingly, organizations are turning to artificial intelligence (AI) solutions that make use of AI agents, commonly known as bots to provide a first-line of automated support through the full range of channels that we use to communicate.

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Cheers!

Susanth Sutheesh

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