Microsoft AI & ML Engineering Professional Certificate Course Syllabus

Overview: This professional certificate program provides a comprehensive introduction to AI and machine learning engineering using Microsoft Azure. Designed for beginners with some technical orientation, the course spans approximately 15–20 weeks of part-time study. Learners will progress through foundational concepts, cloud-based AI development, and practical implementation using Azure tools. Each module combines theory with hands-on labs, culminating in a final project that demonstrates real-world AI/ML engineering skills.

Module 1: Foundations of AI and Machine Learning

Estimated time: 20 hours

• Core components of AI/ML pipelines
• Data pipelines and model development
• Deployment strategies for ML systems
• Engineering mindset for scalable ML solutions

Module 2: Microsoft Azure for AI and Machine Learning

Estimated time: 30 hours

• Building end-to-end ML workflows in Azure
• Developing AI solutions with Azure services
• Managing data across Azure platforms
• Optimizing models in cloud environments

Module 3: Artificial Intelligence on Microsoft Azure

Estimated time: 40 hours

• Computer vision applications using Azure AI
• Natural language processing (NLP) with Azure
• Anomaly detection and conversational AI
• Ethical AI and responsible AI principles

Module 4: Microsoft Azure Machine Learning

Estimated time: 40 hours

• Training predictive models in Azure ML Studio
• Hyperparameter tuning and model optimization
• Using AutoML to accelerate development
• Monitoring and retraining deployed models

Module 5: Model Deployment and Management

Estimated time: 25 hours

• Deploying models to production environments
• Performance monitoring and logging
• CI/CD for machine learning pipelines
• Security and compliance in ML systems

Module 6: Final Project

Estimated time: 35 hours

• Design and build an end-to-end AI solution on Azure
• Implement data pipeline, model training, and deployment
• Document ethical considerations and model performance

Prerequisites

• Basic understanding of programming (Python preferred)
• Familiarity with cloud computing concepts
• Some technical background (not for complete tech novices)

What You'll Be Able to Do After

• Design and manage AI infrastructure on Microsoft Azure
• Build and deploy machine learning models using Azure ML
• Apply AI to real-world problems like computer vision and NLP
• Implement responsible AI practices in enterprise settings
• Create a professional portfolio with hands-on projects