What does architecting LLM systems for production mean in this course?

Architecting LLM systems for production means designing the full system around the model, not just the model itself. In this course, that includes comparing request flows, deployment patterns, resilience choices, and data workflows so an LLM application can run reliably at scale.

When would you use a production architecture approach for an LLM application?

You use this approach when an LLM feature has to support real users and can no longer rely on a simple demo or single-service setup. The course focuses on cases where latency, cost, failure handling, privacy, and operational complexity shape the architecture.

How does production architecture design fit into a broader LLM workflow?

It fits between proving that an LLM feature works and operating it as a dependable service. The course treats architecture design as the layer that connects request handling, service boundaries, deployment choices, and data movement into a repeatable production system.

How is production architecture design different from building an LLM prototype?

Building a prototype is mainly about showing that model behavior is useful, while production architecture design is about how the whole system behaves under load and failure. In this course, that difference shows up in choices like blocking versus background processing, resilience planning, and how services are deployed.

Do you need any prerequisites before learning production architecture design for LLM systems?

A basic understanding of cloud concepts, microservices, and how LLM applications are put together is helpful before you start. What matters most is being comfortable reading system flows and reasoning about latency, reliability, and deployment trade-offs.

What tools, platforms, or methods are used in this course?

The course uses sequence diagrams and structured trade-off analysis to compare architecture choices. It also introduces container orchestration manifests and Airflow workflows as examples of how production designs are deployed and automated.

What specific tasks will you practice or complete in this course?

You practice comparing synchronous and asynchronous request flows, weighing self-hosting against managed APIs, analyzing bottlenecks and single points of failure, and planning resilient deployments. You also create architecture diagrams and outline automated data workflows that can handle schema changes.

Designing Production LLM Architectures

本课程是 LLM Engineering That Works: Prompting, Tuning, and Retrieval 专业证书的一部分

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5个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Compare synchronous and asynchronous architectures and apply 12-factor principles and container orchestration to deploy scalable microservices.
Analyze multi-region deployments, pinpoint latency bottlenecks, and design resilient architecture improvements via fault analysis.
Create Airflow DAGs to automate data workflows and analyze the impact of schema evolution on downstream processes and tests.
Analyze trade-offs between self-hosting models vs. managed APIs and evaluate proposed infrastructure for fault tolerance and cost.

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本课程是 LLM Engineering That Works: Prompting, Tuning, and Retrieval 专业证书专项课程的一部分

在注册此课程时，您还会同时注册此专业证书。

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该课程共有5个模块

This course is for ML engineers, solutions architects, and senior developers who build robust infrastructure powering large language models. This course teaches you how to design, deploy, and maintain the complex, interconnected systems required for scalable, resilient, and cost-effective LLM applications in the real world.

You will learn to think like an architect, starting with foundational design choices. Using sequence diagrams and structured analysis, you will compare synchronous and asynchronous architectures and evaluate the critical trade-offs between self-hosting open-source models and using managed APIs, considering total cost of ownership, latency, and data privacy. The course then dives deep into building for resilience and scale, applying the 12-factor app methodology to design stateless, configurable microservices. You’ll learn to analyze multi-region deployment strategies for fault tolerance and to use container orchestration manifests like Helm to deploy scalable applications capable of handling production workloads. Finally, you’ll master the data backbone of your system by designing automated data pipelines with tools like Airflow and learning to manage the complexities of schema evolution.

单元详情

This module empowers engineers and architects to master the "build vs. buy" decision for LLM applications through a structured, strategic lens. You will learn to design complex system architectures using sequence diagrams to evaluate synchronous and asynchronous processing, while comparing the trade-offs of self-hosted open-source models against managed APIs. By focusing on critical metrics like Total Cost of Ownership (TCO), latency, and data privacy, you will develop the expertise to justify architectural choices. Ultimately, you'll gain the confidence to document and defend high-performance, business-aligned AI solutions to any stakeholder.

涵盖的内容

4个视频2篇阅读材料3个作业

4个视频总计38分钟

The Cost of Ambiguity8分钟
Building Sequence Diagrams Step-by-Step9分钟
The Build vs. Buy Dilemma9分钟
A Practical Guide to TCO Calculation12分钟

2篇阅读材料总计24分钟

Synchronous vs. Asynchronous Architectures12分钟
The Deployment Decision Matrix12分钟

3个作业总计55分钟

Hands-On Learning: Diagram an LLM-Powered Workflow15分钟
Hands-On Learning: Calculate the TCO for Your LLM10分钟
Architectural Decision Record (ADR)30分钟

This module explores building resilient, scalable architectures for LLM applications. You will apply 12-factor app methodology to design portable, cloud-native microservices, mastering stateless design and dependency management. The curriculum bridges theory and practice by evaluating multi-region deployment strategies for fault tolerance and high availability. You'll learn to analyze failover mechanisms and mitigate architectural risks before production. By the end, you’ll be equipped to document reliable, future-proof AI systems. Prerequisites include a foundational understanding of cloud concepts (regions/zones) and microservice basics (containers/APIs).

涵盖的内容

1个视频1篇阅读材料3个作业

This module teaches how to transition LLM prototypes into production-grade services. You will learn to analyze multi-stage architectures like RAG to identify and quantify performance bottlenecks using evidence-based metrics. The curriculum focuses on mastering Kubernetes deployment through declarative Helm charts and implementing Horizontal Pod Autoscaling (HPA) to manage unpredictable traffic. By studying deployment lifecycles, including controlled rollouts and rapid rollbacks, you will gain the skills to transform fragile prototypes into resilient, scalable, and reliable production systems capable of handling real-world loads.

涵盖的内容

5个视频5篇阅读材料6个作业

5个视频总计20分钟

Why Performance is a Pipeline Problem4分钟
How to Trace a Request and Spot Bottlenecks3分钟
How to Quantify Latency from Logs4分钟
Why Prototypes Fail in Production4分钟
How to Write a Helm Chart with Autoscaling6分钟

5篇阅读材料总计24分钟

Deconstructing a RAG Architecture5分钟
Evidence Replaces Assumption: The Power of Profiling4分钟
Interpreting Performance Dashboards5分钟
Declarative Deployments with Helm and Kubernetes4分钟
Anatomy of a Production Helm Chart6分钟

6个作业总计67分钟

Hands-On Learning: Analyze the Architecture Diagram10分钟
Scenario-Based Question: Architectural Analysis10分钟
Hands-On Learning: Analyzing Production Logs to Identify Performance Bottlenecks10分钟
Evidence-Based Performance Tuning Quiz10分钟
Hands-On Learning: Review and Correct the Helm Manifest7分钟
Scalable LLM Deployment Portfolio20分钟

In today's dynamic data landscape, pipelines often break when source data structures change unexpectedly—a problem known as schema drift. This module tackles that challenge head-on, teaching you how to design and automate data pipelines that can gracefully handle schema evolution using Apache Airflow. By the end, you will be equipped to create resilient, scalable, and fully automated data pipelines that are built to withstand the complexities of real-world data environments.

涵盖的内容

5个视频5篇阅读材料7个作业

5个视频总计23分钟

Coding and Scheduling Your First DAG5分钟
The Silent Pipeline Killer: Schema Drift4分钟
Writing and Adapting dbt Tests5分钟
When a Tree Falls: The Danger of Silent Failures3分钟
Building-In Failure Alerts6分钟

5篇阅读材料总计21分钟

The Core Components of Airflow5分钟
How-To: Managing Connections and Variables4分钟
Understanding Schema Drift and Data Lineage3分钟
How-To: Documenting and Communicating Schema Changes4分钟
Designing for Observability5分钟

7个作业总计82分钟

Hands-On Learning: Automating an Article Processing Workflow10分钟
Knowledge Check: Airflow Fundamentals5分钟
Hands-On Learning: Handling Schema Evolution with dbt Testing12分钟
Knowledge Check: Schema Impact5分钟
Hands-On Learning: Enhancing Your DAG with Monitoring and Alerting15分钟
Knowledge Check: Monitoring Concepts5分钟
Building a Resilient and Monitored Pipeline30分钟

In the module, you will step into the high-stakes role of a senior systems engineer tasked with diagnosing a failing AI service. A critical Retrieval-Augmented Generation (RAG) system is plagued by high latency and intermittent outages, and you must get to the root of the problem. Using architectural diagrams, system logs, and performance metrics, you will analyze the system’s design to identify the primary performance bottleneck and the most significant single point of failure. Your analysis will culminate in a concise, two-paragraph report for stakeholders, pinpointing the critical issues and recommending targeted fixes to restore stability and performance.