- AI & Machine Learning
- Al 102
- Microsoft
Microsoft Certified Azure Al Engineer Associate: Al 102
Mastering Microsoft Azure AI Engineering: AI 102
Advanced Azure AI Engineering: AI 102
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Overview
Microsoft Certified Azure Al Engineer Associate: Al 102 Certification training
The Microsoft Certified Azure AI Engineer Associate: AI 102 certification course focuses on advanced skills required for designing and implementing AI solutions on Microsoft Azure. Participants delve into topics such as natural language processing, computer vision, and responsible AI deployment strategies. The course equips professionals with the expertise to build and deploy machine learning models using Azure Machine Learning, Azure Cognitive Services, and other AI tools. By mastering these advanced techniques, certified Azure AI Engineers can effectively address complex business challenges, optimize AI performance, and ensure ethical AI practices in their solutions.
Industry Demand
Unlock Opportunities: Over Microsoft Certified Azure Al Engineer Associate: Al 102 Jobs Available by 2030!
The Microsoft Certified Azure AI Engineer Associate (AI-102) certification is highly sought after as organizations increasingly leverage Azure AI services for advanced analytics, machine learning, and AI solutions. Certified Azure AI Engineers demonstrate proficiency in designing and implementing AI solutions on the Azure platform, including natural language processing, computer vision and machine learning models deployment. Demand for Azure AI Engineers spans industries such as technology, healthcare, and finance, where AI-driven insights and automation are crucial for innovation and competitive advantage.
Salaries for Microsoft Certified Azure AI Engineer Associates vary based on experience and location. Entry-level positions typically offer salaries starting around $90,000 per year, while experienced professionals can earn upwards of $130,000 annually, especially in roles requiring deep expertise in Azure AI services, solution architecture, and AI strategy implementation.
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Azure Al Engineer Associate: Al 102 COURSE PRICING
Tuition Fee
Best Seller
Live Online Classroom
Learn in expert-led live sessions
Solid Experiential Learning
- 32 Contact Hrs with Live Training
- Free On-Demand Course by Industry Experts
- Practice exams and additional resources
-
₹ 24,750₹ 22,500
Self-Paced Learning
Learn at your own pace
Solid Experiential Learning
- 40 Contact Hrs with Live Training
- Practice exams and additional resources
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₹ 22,500₹ 19,250
WHAT YOU’LL LEARN IN THIS training
Learning Objectives
Implement advanced AI solutions using Azure services.
Develop expertise in natural language processing and computer vision.
Deploy machine learning models with Azure Machine Learning.
Optimize AI performance and scalability on Azure.
Apply responsible AI practices and ethical considerations.
PREREQUISITES FOR this CERTIFICATION TRAINING
Prerequisites and Eligibility
- Proficiency in Azure fundamentals and services
- Experience with machine learning models and algorithms
- Familiarity with data engineering concepts
WHO SHOULD ATTEND THiss COURSE ONLINE
Who This Course Is For?
- AI Engineers
- Data Scientists
- Machine Learning Engineers
- AI Developers
- Software Engineers
- IT Professionals
- Cloud Solution Architects
COURSE SYLLABUS
Curriculum
- Understand capabilities of Azure Machine Learning
- Understand capabilities of Azure AI Services
- Understand capabilities of Azure OpenAI Service
- Understand capabilities of Azure AI Search
- Create Azure AI services resources in an Azure subscription
- Identify endpoints, keys, and locations
- Use a REST API and an SDK to consume Azure AI services
- Consider authentication for Azure AI services
- Manage network security for Azure AI services
- Monitor Azure AI services costs
- Create alerts and view metrics for Azure AI services
- Manage Azure AI services diagnostic logging
- Create containers for reuse
- Deploy to a container and secure a container
- Consume Azure AI services from a container
- Provision an Azure AI Vision resource
- Analyze an image
- Generate a smart-cropped thumbnail
- Provision Azure resources for Azure AI Custom Vision
- Understand image classification
- Train an image classifier
- Identify options for face detection, analysis, and identification
- Understand considerations for face analysis
- Detect faces with the Computer Vision service
- Understand capabilities of the Face service
- Compare and match detected faces
- Implement facial recognition
- Read text from images using OCR
- Use the Azure AI Vision service Image Analysis with SDKs
- Develop an application that can read printed and handwritten text
- Describe Azure Video Indexer capabilities
- Extract custom insights
- Use Azure Video Indexer widgets and APIs
- Detect language from text
- Analyze text sentiment
- Extract key phrases, entities, and linked entities
- Understand question answering and how it compares to language understanding
- Create, test, publish, and consume a knowledge base
- Implement multi-turn conversation and active learning
- Create a question answering bot to interact with using natural language
- Provision Azure resources for Azure AI Language resource
- Define intents, utterances, and entities
- Use patterns to differentiate similar utterances
- Use pre-built entity components
- Train, test, publish, and review an Azure AI Language model
- Understand types of classification projects
- Build a custom text classification project
- Tag data, train, and deploy a model
- Submit classification tasks from your own app
- Understand tagging entities in extraction projects
- Understand how to build entity recognition projects
- Provision a Translator resource
- Understand language detection, translation, and transliteration
- Specify translation options
- Define custom translations
- Provision an Azure resource for the Azure AI Speech service
- Use the Azure AI Speech to text API to implement speech recognition
- Use the Text to speech API to implement speech synthesis
- Configure audio format and voices
- Use Speech Synthesis Mark-up Language (SSML)
- Provision Azure resources for speech translation
- Generate text translation from speech
- Synthesize spoken translations
- Create an Azure AI Search solution
- Develop a search application
- Implement a custom skill for Azure AI Search
- Integrate a custom skill into an Azure AI Search skillset
- Create a knowledge store from an Azure AI Search pipeline
- View data in projections in a knowledge store
- Describe Azure AI Document Intelligence solution components
- Create and connect to Azure AI Document Intelligence resources
- Choose whether to use a prebuilt, custom, or composed model
- Identify business problems by using prebuilt models in Forms Analyzer
- Analyze forms by using the General Document, Read, and Layout models
- Analyze forms by using financial, ID, and tax prebuilt models
- Identify how Document intelligence's service and models can automate processes
- Use Document intelligence's capabilities with SDKs, and REST API
- Develop and test custom models
- Create an Azure OpenAI Service resource
- Deploy a base model and test it in the Studio's playgrounds
- Generate completions to prompts and begin to manage model parameters
- Integrate Azure OpenAI into your application
- Differentiate between different endpoints available to your application
- Generate completions to prompts using the REST API
- Understand the concept of prompt engineering
- Know how to design and optimize prompts to better utilize AI models
- Use natural language prompts to write code
- Build unit tests and understand complex code with AI models
- Generate comments and documentation for existing code
- Describe the capabilities of DALL-E in the Azure OpenAI service
- Use the DALL-E playground in Azure OpenAI Studio
- Integrate DALL-E image generation into your apps
- Describe the capabilities of Azure OpenAI on your data
- Configure Azure OpenAI to use your own data
- Use Azure OpenAI API to generate responses based on your own data
- Describe an overall process for responsible generative AI solution development
- Identify and prioritize potential harms relevant to a generative AI solution
- Measure the presence of harms in a generative AI solution
- Mitigate harms in a generative AI solution
- Prepare to deploy and operate a generative AI solution responsibly
Information Related To Exam
Exam Information
- Exam Format- Multiple Choice questions
- Questions Question Count- 40-60 questions
- Exam Duration- 120 Minutes
- Passing Score: 700 / 1000
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Al 102 CERTIFICATION COURSE REVIEWS
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Al 102 CERTIFICATION FAQS
Frequently Asked Questions
The AI-102 certification validates your expertise in designing and implementing AI solutions on Microsoft Azure. It demonstrates your ability to utilize Azure AI services, machine learning models, and data processing technologies to build scalable AI solutions.
This certification is suitable for AI Engineers, Data Scientists, AI Developers, and IT professionals involved in designing and implementing AI solutions using Azure services. It is ideal for those looking to enhance their skills in AI development and deployment on the Azure platform.
Preparation involves gaining hands-on experience with Azure AI services such as Azure Cognitive Services, Azure Machine Learning, and Azure Databricks. Additionally, studying official Microsoft learning paths, practicing with sample questions, and exploring real-world AI scenarios are recommended.
Achieving this certification enhances your credibility as an AI Engineer specializing in Azure technologies. It validates your skills in designing and implementing AI solutions, qualifies you for roles demanding Azure AI expertise, and opens up career opportunities in AI development and cloud computing.
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