About this Course
This course provides a comprehensive introduction to fundamental components of artificial intelligence and machine learning (AI & ML) infrastructure. You will explore the critical elements of AI & ML environments, including data pipelines, model development frameworks, and deployment platforms. The course emphasizes the importance of robust and scalable design in AI & ML infrastructure. By the end of this course, you will be able to: 1. Analyze, describe, and critically discuss the critical components of AI & ML infrastructure and their interrelationships. 2. Analyze, describe, and critically discuss efficient data pipelines for AI & ML workflows. 3. Analyze and evaluate model development frameworks for various AI & ML applications. 4. Prepare AI & ML models for deployment in production environments. To be successful in this course, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended
What You'll Learn
- Analyze and describe critical components of AI and ML infrastructure and their relationships
- Evaluate efficient data pipelines for AI and ML workflows
- Analyze and assess model development frameworks for AI and ML applications
- Prepare AI and ML models for deployment in production environments
Prerequisites
- Intermediate programming knowledge of Python
- Basic understanding of statistics
- General knowledge of AI and ML concepts
Instructors
Microsoft