Architect AI Solutions: From Needs to Models
By External Provider
About this course
Designing effective AI systems requires more than model knowledge—it requires the ability to translate business goals into technical architectures that are scalable, practical, and aligned with stakeholder expectations. In this intermediate course, you will learn how to analyze real stakeholder requirements and map them to appropriate AI approaches, whether that involves managed APIs, cloud-native AI services, or custom machine learning models. You will also design complete solution architectures that integrate third-party tools, vector databases, transformer-based ranking models, and orchestration layers to deliver end-to-end functionality. Through hands-on labs and scenario-driven exercises, you will practice making architectural decisions, evaluating trade-offs, and communicating your reasoning clearly. By the end, you will be equipped to architect AI solutions that balance accuracy, cost, performance, and time-to-market.
What you will learn
- Analyze stakeholder requirements and map them to appropriate AI approaches including managed APIs, cloud services, or custom ML models.
- Design end-to-end AI solution architectures integrating vector databases, transformer models, and orchestration layers to meet business goals.