ADaSci Academy

    Building AI Systems with Microservices on AWS

    This course explores the convergence of AI and microservices, teaching you to design scalable, cloud-native AI systems using AWS. Learn RAG, agentic workflows, latency optimization, state management, and production-ready…

    4,909+ enrolled
    3 hours
    Intermediate
    Building AI Systems with Microservices on AWS
    01 — Overview

    About this course

    This course explores the convergence of AI and microservices, teaching you to design scalable, cloud-native AI systems using AWS. Learn RAG, agentic workflows, latency optimization, state management, and production-ready deployment patterns for enterprise-grade intelligent applications.

    02 — Outcomes

    What you'll learn

    ✓
    Architect AI-native microservices that integrate LLMs, RAG, and agentic workflows into enterprise systems.
    ✓
    Design for latency, state management, and probabilistic-deterministic integration challenges
    ✓
    Implement event-driven, serverless AI architectures using AWS (Bedrock, SageMaker, Lambda, EKS/ECS).
    ✓
    Build intelligent customer support systems with feedback loops, observability, and production-grade governance.
    03 — Curriculum

    Course content

    5 sections · 8 lectures

    From Monolithic Legacies to AI-Native Microservices
    40m
    From Monolithic Legacies to AI-Native Microservices - Slide Deck (PDF)
    0
    Challenges of Latency, Determinism and State
    33m
    Challenges of Latency, Determinism and State - Slide Deck (PDF)
    0
    Popular Patterns and Best Patterns in Microservices
    35m
    Popular Patterns and Best Practices in Microservices - Slide Deck (PDF)
    0
    Hands-On Project on AWS
    30m
    Feedback and Continuous Improvement
    0
    04 — Prerequisites

    Requirements

    • Basic understanding of cloud computing (preferably AWS fundamentals).
    • Familiarity with microservices concepts (REST APIs, containers, distributed systems).
    • Introductory knowledge of AI/ML or LLM concepts (helpful but not deeply mathematical).
    • Comfort with system architecture diagrams and high-level technical discussions.
    05 — Audience

    Who should take this course

    • Cloud Architects designing AI-enabled enterprise systems.
    • Backend / Microservices Developers integrating LLM APIs.
    • AI/ML Engineers deploying models in production.
    • DevOps / Platform Engineers managing distributed AI workloads.
    • Technical Product Managers leading AI transformation initiatives.
    • Engineering Leaders modernizing legacy systems into AI-native architectures.

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    Duration3h
    LevelIntermediate

    $49.00