Analyze & Deploy Scalable LLM Architectures is an intermediate course for ML engineers and AI practitioners tasked with moving large language model (LLM) prototypes into production. Many powerful models fail under real-world load due to architectural flaws. This course teaches you to prevent that.
Analyze & Deploy Scalable LLM Architectures
Analyze & Deploy Scalable LLM Architectures
This course is part of Microservices Architecture for AI Systems Specialization
Instructor: LearningMate
Access provided by ExxonMobil
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
- Performance Analysis
- Application Deployment
- Cloud Deployment
- Continuous Delivery
- Release Management
- Model Deployment
- Retrieval-Augmented Generation
- Performance Testing
- Scalability
- Large Language Modeling
- Systems Analysis
- Analysis
- MLOps (Machine Learning Operations)
- Configuration Management
- Performance Tuning
- Containerization
- Infrastructure as Code (IaC)
- Application Performance Management
- LLM Application
- Kubernetes
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Taught in English
Recently updated!
January 2026
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This course is part of the Microservices Architecture for AI Systems Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
This module establishes the foundational mindset that "performance lives in the pipeline." Learners will discover that a large language model (LLM) application is a multi-stage system where overall speed is dictated by the slowest component. They will learn to deconstruct a complex Retrieval-Augmented Generation (RAG) architecture, trace a user request through it, and use system diagrams to form an evidence-based hypothesis about the primary performance bottleneck.
What's included
2 videos1 reading2 assignments
In this module, learners move from hypothesis to evidence. They will learn to use system logging and profiling data to quantify the precise latency contribution of each stage in an LLM pipeline. The focus is on designing small, reversible, and hypothesis-driven experiments to prove or disprove their initial findings and distinguish a performance bottleneck's root cause from its symptoms.
What's included
1 video2 readings2 assignments
This module bridges the gap between a working prototype and a resilient, production-ready service. Learners will design and manage declarative deployments using Helm and Kubernetes, package a multi-component RAG stack, and implement Horizontal Pod Autoscaling (HPA) for dynamic, cost-efficient scaling. They will also master the critical operational skills of performing controlled, zero-downtime rollouts and rapid rollbacks.
What's included
2 videos2 readings2 assignments
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