Expert Tailored AI & Machine Learning Package

This exam is an opportunity for you to demonstrate knowledge of machine learning and AI concepts and related Microsoft Azure services. As a candidate for this exam, you should have familiarity with Exam AI-900’s self-paced or instructor-led learning material.

This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:

• Cloud basics
• Client-server applications

You can use Azure AI Fundamentals to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

• Describe Artificial Intelligence workloads and considerations (20–25%)
• Describe fundamental principles of machine learning on Azure (25–30%)
• Describe features of computer vision workloads on Azure (15–20%)
• Describe features of Natural Language Processing (NLP) workloads on Azure (25–30%)

Python is developed under an OSI-approved open source license, making it freely usable and distributable, even for commercial use. Python is a general-purpose programming language. Created nearly 30 years ago, it is now one of the most popular languages out there to use. Its popularity is particularly important in the data science and machine learning fields. But it is also a language that is easy to learn, and that’s why it has become the language most taught in universities.

Python interpreters are available for the main operating systems as well (Linux, Mac OS, Windows, Android, iOS, BSD, etc.) so it’s very flexible in where it is used. Python has a simple syntax that makes it suitable for learning to program as a first language. The learning curve is smoother than other languages such as Java, which quickly requires learning about Object Oriented Programming or C/C++ that require understanding pointers. Still, it's possible to learn about OOP or functional programming in Python when the time comes.

Where is Python Used?

• Web Development, using the frameworks Django, Flask, Pylons
• Data Science and Visualization using Numpy, Pandas and Matplotlib
• Machine learning with Tensorflow and Scikit-learn
• Desktop applications with PyQt, Gtk, wxWidgets and many more
• Mobile applications using Kivy or BeeWare
• Education: Python is a great language to learn programming

Learn about the exciting world of ChatGPT and AI for Business to apply immediately for business content requirements.

(Beginner) This course has been designed for anyone who would like to grasp how the world is adapting to ChatGPT which is an Open AI platform revolutionizing the way we engage with artificial intelligence. An AI-powered Chatbot can have natural-sounding conversations with users and is used to comprehend queries and give precise answers.

Did You Know that following the release of ChatGPT, Open AI was valued at $29 billion.

This course will guide you through the all the media hype and provide you with the right foundation to understand the world of ChatGPT.

The course will provide the learner demonstrations on how to immediately use ChatGPT to create content and derive value from using this AI service.

Azure OpenAI Service provides access to OpenAI's powerful large language models such as GPT; the model behind the popular ChatGPT service. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio. In this course, you'll learn how to provision Azure OpenAI service, deploy models, and use them in generative AI applications.

The audience for this course includes software developers and data scientists who need to use large language models for generative AI. Some programming experience is recommended, but the course will be valuable to anyone seeking to understand how the Azure OpenAI service can be used to implement generative AI solutions.

Prerequisites

Before starting this learning path, you should already have:

• Familiarity with Azure and the Azure portal.

• Introduction to Azure OpenAI Service
• Get started with Azure OpenAI Service
• Build natural language solutions with Azure OpenAI Service
• Apply prompt engineering with Azure OpenAI Service

As a Microsoft Azure AI engineer, you build, manage, and deploy AI solutions that make the most of:

• Azure Cognitive Services.
• Azure Applied AI services.

Your responsibilities include participating in all phases of AI solutions development, including:

• Requirements definition and design
• Development
• Deployment
• Integration
• Maintenance
• Performance tuning
• Monitoring

You work with solution architects to translate their vision and with data scientists, data engineers, Internet of Things (IoT) specialists, infrastructure administrators, and other software developers to build complete end-to-end AI solutions.

As an Azure AI engineer, you have experience developing solutions that use languages such as:

• Python
• C#

You should be able to use Representational State Transfer (REST) APIs and SDK to build secure image processing, video processing, natural language processing, knowledge mining, and conversational AI solutions on Azure. You should be familiar with all methods of implementing AI solutions. Plus, you understand the components that make up the Azure AI portfolio and the available data storage options. As an Azure AI engineer, you also need to understand and be able to apply responsible AI principles.

Skills Measured

• Plan and manage an Azure AI solution (25–30%)
• Implement image and video processing solutions (15–20%)
• Implement natural language processing solutions (25–30%)
• Implement knowledge mining solutions (5–10%)
• Implement conversational AI solutions (15–20%)