The value of a Microsoft Azure AI Engineer lies in their ability to design, develop, and deploy intelligent solutions on the Microsoft Azure platform. AI solutions are increasingly becoming an essential part of many businesses, and Azure AI Engineers play a crucial role in helping organizations automate and optimize their processes, improve their customer experience, and gain valuable insights from their data. As a result, Microsoft has always provided the finest solutions for a variety of sectors in future technologies such as artificial intelligence (AI).
These areas assist many top organizations and businesses because they decrease effort, expenses, and resources. As a result, Microsoft provides a number of AI certifications to assist people to learn more about these topics and further their careers as associate Azure AI engineers. You may utilize this to get AI knowledge and abilities to start your career.
But, knowing about the role and certification is one thing, and achieving that role is another. That is to say, to help you earn the role of Azure AI Engineer, in this blog, we will be talking about the top methods and resources that will help you achieve your dream job role.
Coming on to the role of Azure AI Engineer.
Who is an Azure AI Engineer?
Azure AI Engineers is a professional who is responsible for designing, building, and deploying AI solutions on the Microsoft Azure platform. They use a variety of tools and technologies such as machine learning, deep learning, natural language processing, and computer vision to develop intelligent applications that can automate and optimize business processes.
Some of the key responsibilities of a Microsoft Azure AI Engineer include:
- Identifying and understanding the business problems that can be solved with AI solutions.
- Designing and developing AI models using Azure Machine Learning Studio and other Azure tools.
- Collecting and processing data to feed into AI models.
- Evaluating the performance of AI models and fine-tuning them as necessary.
- Deploying AI models as web services on Azure.
- Integrating AI solutions with other applications and services on Azure.
- Ensuring the security and compliance of AI solutions.
Talking about the salary:
In India, the average yearly income for an entry-level AI engineer is around 8 lakhs, much higher than the average wage for any other engineering graduate. The compensation of an AI engineer at the highest level might be as much as 50 lakhs.
While the statistics differ, one thing is certain: an AI engineer’s average annual compensation is far over $100,000. According to Source data, the average national wage in the United States is $114,121, with a low of $78,000 and a high of $150,000.
Ways to become Microsoft Azure AI Engineer
To become a Microsoft Azure AI Engineer, one must have a solid understanding of machine learning algorithms, programming languages such as Python and R, and Azure tools and services. You must have experience in designing and developing AI solutions for various business problems.
- A degree in computer science, engineering, mathematics, or a related field is preferred for this role. However, it is also possible to become an Azure AI Engineer through self-study and relevant work experience.
- You should have a good grasp of programming languages such as Python, R, and SQL. You should also have experience with data manipulation, analysis, and visualization.
- Get familiar with Microsoft Azure and its various services such as Azure Machine Learning, Cognitive Services, and Bot Framework.
- Gain practical experience in developing AI solutions using Azure tools and services. Participate in hackathons, and coding challenges, or contribute to open-source projects related to AI.
The most significant aspect of this journey will be the development of abilities and a thorough comprehension of the concepts. alarmed. We’ll go through how to become an Azure AI Engineer for both beginners and experts using Microsoft Certification. Possessing the appropriate skills and expertise is the most important factor.
1. Getting Microsoft Certified
The best way to cover the skills is to become certified. This will not only improve your knowledge and skills but will also boost your market value. In the section below, you’ll find Microsoft certifications related to the role of Azure AI Engineer that you can look into based on your knowledge and skills.
Designing and Implementing a Microsoft Azure AI Solution: AI-102 Exam
For becoming a Microsoft Certified Azure AI Engineer, it is necessary to pass the AI-102 exam. Using Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework, this exam verifies candidates’ abilities to design, manage, and deploy AI applications. An Azure AI engineer associate, on the other hand, is in charge of taking part in all stages of developing AI solutions, such as deployment, upkeep, performance tweaking, and monitoring.
Further, they also operate with solution architects for converting their vision and with data scientists, data engineers, IoT specialists, and AI developers for creating complete end-to-end AI solutions.
Knowledge requirement for the exam:
- Firstly, you must learn C# or Python in order to pass this exam.
- Secondly, you must be able to create computer vision, natural language processing, knowledge mining, and conversational AI applications on Azure using REST-based APIs and SDKs.
- Lastly, it’s essential to comprehend the components that make up the Azure AI portfolio, as well as the data storage solutions offered. Furthermore, candidates must understand and be able to apply ethical AI principles.
Skills Measured:
AI-102 exam validates the candidate’s skills for executing tasks like:
- Firstly, planning and controlling Azure Cognitive Services solutions
- Secondly, applying Computer Vision solutions and natural language processing solutions
- Lastly, applying knowledge mining solutions
- Lastly, apply conversational AI solutions.
Consider this as an option exam for those who are new in the AI field. This exam will help in learning the basics of artificial intelligence.
Exam AI-900: Microsoft Azure AI Fundamentals
The AI-900 test is for candidates who have a good understanding of machine learning (ML) and artificial intelligence (AI) principles, as well as the Microsoft Azure services that support them. This exam allows you to demonstrate your understanding of popular machine learning and artificial intelligence workloads, as well as how to deploy them on Azure. Furthermore, this exam is designed for both technical and non-technical candidates. And, any programming knowledge or experience would be advantageous.
2. Microsoft Learning Path
Microsoft also offers new ways to understand the topics. That is, for each exam, Microsoft provides a variety of learning routes, each of which covers the exam’s contents in modules. These contain all relevant data as well as helpful reference links.
1. Preparing for AI engineering
As an aspiring Azure AI Engineer, you should be familiar with some of the major basic principles that AI is built on, as well as the many Microsoft Azure services that you may utilize to build AI solutions.
Prerequisites:
You should already be familiar with Azure and the Azure interface, as well as a fundamental understanding of software engineering, before beginning this learning route.
2. Provisioning and managing Azure Cognitive Services
You’ll learn how to supply and secure in this learning route. monitor, install and employ cognitive services resources to create intelligent solutions
Prerequisites:
You should have the following knowledge before beginning this learning path:
- Firstly, knowledge of Azure and the Azure portal.
- Secondly, programming experience in C# or Python. However, if you’ve never programmed before, we recommend starting with the Take Your First Steps with C# or Take Your First Steps with Python learning paths before moving on to this one.
3. Processing and translating the text with Azure Cognitive Services
You can design apps that extract semantic meaning from text and translate it between languages using Azure Cognitive Services.
Prerequisites:
You should have the following knowledge before beginning this learning path:
4. Processing and Translating Speech with Azure Cognitive Speech Services
Using the Speech service, you may learn how to create speech-enabled applications.
Prerequisites:
You should have the following knowledge before beginning this learning path:
- Firstly, knowledge of Azure and the Azure portal.
- Secondly, programming experience in C# or Python. However, if you’ve never programmed before, we recommend starting with the Take Your First Steps with C# or Take Your First Steps with Python learning paths before moving on to this one.
5. Creating a Language Understanding solution
Language models are used in natural language processing (NLP) systems to interpret the semantic meaning of written or spoken language. The Language Understanding service can be used to create language models for your applications.
Prerequisites:
You should have the following knowledge before beginning this learning path:
- Firstly, knowledge of Azure and the Azure portal.
- Secondly, programming experience in C# or Python. However, if you’ve never programmed before, we recommend starting with the Take Your First Steps with C# or Take Your First Steps with Python learning paths before moving on to this one.
6. Creating a QnA solution
The ability for users to ask inquiries in natural language and receive suitable responses is a typical characteristic of intelligent apps.
Prerequisites:
You should have the following knowledge before beginning this learning path:
- Firstly, knowledge of Azure and the Azure portal.
- Secondly, programming experience in C# or Python. However, if you’ve never programmed before, we recommend starting with the Take Your First Steps with C# or Take Your First Steps with Python learning paths before moving on to this one.
7. Creating conversational AI solutions
Conversational AI solutions are built on interactions between humans and bots, or artificial intelligence agents. You’ll learn how to create bots that can be delivered on Microsoft Azure in this learning route.
Prerequisites:
You should have the following knowledge before beginning this learning path:
- Firstly, knowledge of Azure and the Azure portal.
- Secondly, programming experience in C# or Python. However, if you’ve never programmed before, we recommend starting with the Take Your First Steps with C# or Take Your First Steps with Python learning paths before moving on to this one.
8. Creating computer vision solutions with Azure Cognitive Services
The field of artificial intelligence known as computer vision deals with visual perception. Multiple services are included in Azure Cognitive Services that enable popular computer vision scenarios.
Prerequisites:
You should have the following knowledge before beginning this learning path:
- Firstly, knowledge of Azure and the Azure portal.
- Secondly, programming experience in C# or Python. However, if you’ve never programmed before, we recommend starting with the Take Your First Steps with C# or Take Your First Steps with Python learning paths before moving on to this one.
9. Extracting text from images and documents
Learn how to use the form recognizer service’s OCR Test Tool, pre-built models, and custom models to develop text extraction solutions using photos and documents.
Prerequisites:
- Active Azure account
- Knowledge of Azure portal navigation and at least one programming language (C#, Python)
10. Applying knowledge mining with Azure Cognitive Search
You can extract critical insights from this data and enable programs to search and analyze it using Azure Cognitive Search.
Prerequisites:
- Experience with Microsoft Azure and application development in C# or Python is a plus.
3. Microsoft Instructor-led Training
Designing and Implementing an Azure AI Solution (AI-102) exam suitable for software developers who want to build AI-infused applications that supports Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. However, this Microsoft AI-102 training course will use C# or Python as the programming language.
Target Audience
- Firstly, those have knowledge and skills to build, manage and deploy AI solutions that support Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
- Secondly, those with have familiarity with C# or Python.
- Lastly, candidates with knowledge of using REST-based APIs for building computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.
4. Online Study Groups
Candidates can benefit from online study groups when preparing for exams. Joining study groups, in other words, will allow you to stay in touch with experts and professionals who are already on this route. You may also use this group to discuss your exam-related question or issue, as well as take the AI-102 exam study notes.
5. Using Practice Tests
It is critical to practice exams in order to improve your test preparation. By testing yourself with Microsoft AI-102 practice examinations, you will discover your weak and strong areas. Additionally, you will be able to enhance your response abilities, allowing you to save substantial time during the exam. It’s best to take the AI-102 exam practice tests once you’ve finished a whole topic and then take the mock examinations. This will also aid in successful revision. So, go online and search for the best practice tests to assist you in studying for the Microsoft AI-102 certification exam.
Final Words
In conclusion, there will surely be increased demand for an Azure AI Engineer Associate Certification. Many firms’ embrace of cloud computing, as well as the tremendous flood of data from many platforms, will necessitate remarkable business solutions. However, we thoroughly examined Microsoft Azure AI Engineer, including its features, advantages, and instructions for getting started with artificial intelligence. This job will test your knowledge, competence, and ability to do a variety of duties. To put it another way, you must focus on all of the essential areas if you want to increase your preparedness. Create a study schedule and pattern based on the materials you’ve been given, and then begin the preparation process gradually.
