AI/ML Infrastructure
The ability to design AI/ML infrastructure is the most in-demand architecture skill of 2026, yet most engineers struggle to move beyond proof-of-concept Jupyter notebooks to production-grade systems. This path bridges that gap with five challenges that cover the full AI/ML infrastructure stack: RAG pipelines for grounding LLMs in enterprise data, multi-agent orchestration for complex reasoning workflows, model serving platforms for low-latency inference, AI gateway security for responsible AI deployment, and feature stores for consistent ML feature management. Each challenge uses Amazon Bedrock and SageMaker alongside supporting AWS services, teaching you to design systems that are not just technically sound but also cost-effective, observable, and compliant with enterprise governance requirements.
AWS Services Across This Path
5 Challenges in This Path
RAG Pipeline Architecture
Design a Retrieval-Augmented Generation pipeline that grounds LLM responses in enterprise knowledge bases.
Multi-Agent Orchestration
Design a multi-agent system where specialized AI agents collaborate to solve complex tasks.
ML Model Serving Platform
Design a model serving platform that delivers low-latency predictions with A/B testing and canary deployment.
AI Gateway Security Layer
Design a security gateway that enforces responsible AI policies, rate limits, and content filtering for LLM APIs.
ML Feature Store
Design a feature store that serves consistent ML features for both training pipelines and real-time inference.
Ready to start AI/ML Infrastructure?
Each challenge gives you a real scenario, real AWS services, and automated validation. Complete the path and add verified system design experience to your portfolio.
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