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Optimizing and Deploying LLM Systems · LearnSpace
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Optimizing and Deploying LLM Systems

Курс от Edureka
Продвинутый≈ 10.9 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course advances your skills from building working LLM prototypes to scaling, integrating, and deploying production-grade AI systems. You’ll blend system-level concepts with hands-on engineering to profile performance, integrate real-time data and multimodal sources, and ship secure, cloud-deployed applications. Whether you’re a developer, data scientist, or AI practitioner, this course gives you a clear roadmap to transform optimized LangChain workflows into reliable, observable services that interact with live APIs, structured data, and orchestration frameworks. Through guided lessons, structured demonstrations, and project-based learning, you’ll learn how to profile latency and token usage, design efficient prompts and chains, and evaluate pipelines with LLMOps metrics. You’ll connect external APIs, build hybrid retrieval across text, tables, and images, and orchestrate complex data flows using LlamaIndex and LangGraph. Finally, you’ll containerize and deploy a FastAPI service with authentication, monitoring, and CI/CD, culminating in an end-to-end capstone deployment. By the end of this course, you will be able to: • Profile and optimize LLM pipelines for latency, throughput, and token/cost efficiency. • Design prompt and chain strategies (dynamic templates, caching, auto-tuning) to improve reliability and speed. • Implement memory, tools, and agents to enable contextual, goal-oriented behavior. • Integrate real-world data via secure APIs and hybrid retrieval across structured, unstructured, and multimodal sources. • Orchestrate data and evaluation workflows using LlamaIndex and LangGraph for scalable reasoning. • Build, secure, containerize, and deploy a FastAPI service with JWT/OAuth, monitoring, and CI/CD automation. This course is ideal for AI developers, data scientists, and software engineers ready to move beyond prompt experimentation and deliver production-ready LLM applications. A working knowledge of Python and APIs is recommended; all steps are guided to help you master the deployment stack. Join us to learn the engineering patterns that power modern, scalable generative AI—from optimization and orchestration to secure cloud deployment.

Навыки, которые вы освоите

ContainerizationApplication Programming Interface (API)Prompt EngineeringModel DeploymentPostman API PlatformOpenAIApplication DeploymentAuthenticationsPrompt PatternsLLM ApplicationPerformance AnalysisLangChainContinuous DeploymentArtificial IntelligenceAmazon Web ServicesLangGraphLarge Language ModelingRetrieval-Augmented GenerationToken OptimizationContinuous Integration

Программа курса

4 модулей · 65 учебных материалов

01Scaling and Optimizing LLM Pipelines22 материалов

Performance Optimization Fundamentals

Specialization IntroductionВидеоCourse IntroductionВидеоWelcome to Optimizing and Deploying LLM SystemsЧтениеAssessing Prior Knowledge of LLM SystemsDIALOGUE

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Edureka

Преподаватель курса

Optimizing and Deploying LLM Systems
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Обучение на Coursera

≈ 10.9 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский, Венгерский

Часть программы вашего университета
Why Optimization Matters in LLM SystemsВидео
Demonstration: Profiling Response Latency and Token Usage in LangChain AppВидео
Demonstration: Implement Async Batching and Caching Видео
Introduce YourselfОбсуждение
Cost and Latency Optimization GuideЧтение
Practice Quiz: Performance Optimization FundamentalsЗадание

Prompt and Chain Optimization

Efficient Prompts for Reliability and SpeedВидеоDemonstration: Dynamic Prompts and Templates for Better ControlВидеоDemonstration: Implement Prompt Caching and Auto-Tuning ВидеоPrompt Compression and Evaluation MetricsЧтениеPractice Quiz: Prompt and Chain OptimizationЗадание

Evaluating and Monitoring Pipelines

Evaluating Model Output QualityВидеоDemonstration: LangSmith + Weights and Biases IntegrationВидеоDemonstration: Tracking API Costs and Token Usage ВидеоLLMOps Evaluation FrameworksЧтениеPractice Quiz: Evaluating and Monitoring PipelinesЗадание

Module Wrap-Up and Assessment

Summary of Scaling and Optimizing LLM PipelinesЧтение Knowledge Check: Scaling and Optimizing LLM PipelinesЗадание
02Integrating APIs and External Data Sources17 материалов

API-Driven LLM Workflows

Power of APIs in LLMsВидеоDemonstration: Connecting Multiple External APIsВидеоDemonstration: Event-Driven Pipeline with Webhooks and Queues ВидеоSecure API Integration and GovernanceЧтениеPractice Quiz: API-Driven LLM WorkflowsЗадание

Structured and Multi-Modal Data Integration

Combining Structured and Unstructured DataВидеоDemonstration:Natural-Language to SQL with LangChain and OpenAIВидеоDemonstration: Hybrid Retrieval Using LLM and LangChainВидеоMulti-Modal Data FusionЧтениеPractice Quiz: Structured and Multi-Modal Data IntegrationЗадание

Data Orchestration with LlamaIndex and LangGraph

Data Indexing and Workflow OrchestrationВидеоDemonstration: Complex Data Pipeline with LlamaIndexВидеоDemonstration: Automated Evaluation Workflow with LangGraph and LLMВидеоCombining Multiple Data Sources for ReasoningЧтениеPractice Quiz: Data Orchestration with LlamaIndex and LangGraphЗадание

Module Wrap-Up and Assessment

Summary of Integrating APIs and External Data SourcesЧтениеKnowledge Check: Integrating APIs and External Data SourcesЗадание
03Deploying and Managing LLM Applications20 материалов

Building an LLM API Service

From Development to Production — API DesignВидеоDemonstration: Creating REST Endpoints with FastAPI for LangChain WorkflowsВидеоDemonstration: Adding Auth (JWT/OAuth) and Rate LimitingВидеоSecure API ArchitectureЧтениеPractice Quiz: Building an LLM API ServiceЗадание

Containerization and Cloud Deployment

Containerization Essentials for AI AppsВидеоDemonstration: Dockerize LangChain + FastAPI AppВидеоDemonstration: Deployment of API on AWSВидеоSecrets and Environment Configurations in CloudЧтениеPractice Quiz: Containerization and Cloud DeploymentЗадание

End-to-End LLM System Deployment

Capstone Overview: LLM OrchestratorВидеоDemonstration: Capstone Project Overview and ArchitectureВидеоDemonstration: Building LLM APIs with FASTAPIВидеоDemonstration: Authentication and Analytics IntegrationВидеоDemonstration: Data Pipeline and Docker SetupВидеоDemonstration: Automating Deployment with CI/CDВидео

Module Wrap-Up and Assessment

Summary of Deploying and Managing LLM ApplicationsЧтение Deployed LLM System Evaluation ReportЗадание
04Course Wrap-Up6 материалов
Course SummaryВидеоPractice Project: Containerized AI Pipeline using FastAPI and LlamaIndexЧтениеTest your LLM Systems and Deployment skillsDIALOGUEKnowledge Check: Optimizing and Deploying LLM SystemsЗаданиеArchitecting and Deploying Your Production-Grade LLM SystemDIALOGUEDescribe your Learning JourneyОбсуждение
Demonstration: Cloud Deployment and Frontend SetupВидео
End-to-End LLM System DeploymentЗадание