Course Overview
The Microsoft Certified: AI Engineer Associate (AI-102) course by Stalwart Learning is designed for professionals who want to build and deploy AI solutions using Microsoft Azure. This course provides an in-depth understanding of implementing machine learning models, natural language processing, computer vision, and conversational AI on the Azure platform. Participants will gain practical skills in designing, developing, and deploying AI-powered applications, preparing them for the AI-102 certification exam. By the end of the course, learners will be equipped to work with Azure Cognitive Services, Azure Machine Learning, and other Azure AI tools to create cutting-edge AI solutions.
Duration
40 hours (5 days)
Prerequisites
- Experience with Azure and cloud computing services.
- Basic understanding of machine learning concepts and programming languages like Python or C#.
- Familiarity with data engineering, including data preparation and transformation techniques.
Course Outline
1. Introduction to Azure AI Solutions
- Overview of Azure AI services and tools (Azure Cognitive Services, Azure Machine Learning)
- Roles and responsibilities of an AI Engineer in an Azure environment
2. Designing AI Solutions
- Identifying appropriate AI services based on solution requirements
- Designing end-to-end AI solutions for business use cases
- Integrating AI into existing systems
3. Implementing Computer Vision Solutions
- Using Azure Cognitive Services for image and video analysis (Computer Vision API, Face API)
- Building custom vision models and training them for specific use cases
- Implementing optical character recognition (OCR) and object detection
4. Implementing Natural Language Processing (NLP)
- Leveraging Azure Cognitive Services for text analysis (Text Analytics API, Language Understanding (LUIS))
- Implementing chatbots using Azure Bot Services
- Working with sentiment analysis, entity recognition, and key phrase extraction
5. Implementing Conversational AI
- Designing conversational AI solutions with Azure Bot Framework
- Integrating bots into web, mobile, and other applications
- Leveraging QnA Maker and Dialogflow for building conversational interfaces
6. Implementing Machine Learning Models
- Using Azure Machine Learning Studio to build, train, and deploy machine learning models
- Automating machine learning (AutoML) and hyperparameter tuning
- Evaluating and improving model performance
7. Deploying and Managing AI Solutions
- Deploying AI models as APIs in Azure
- Managing and monitoring deployed models with Azure Machine Learning and Application Insights
- Ensuring scalability and performance for production-ready AI models
8. Ethical AI and Responsible AI Practices
- Implementing fairness and transparency in AI solutions
- Ensuring data privacy, security, and compliance in AI models
- Understanding and addressing bias in AI applications
8. Exam Preparation and Practice Tests
- AI-102 exam structure, study tips, and preparation strategies
- Mock exams and scenario-based questions