К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Essential Guide to LLMOps · LearnSpace
Назад в каталог
courseraПрограммирование

Essential Guide to LLMOps

Курс от Packt
Продвинутый≈ 12 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Large Language Models have transformed modern AI workflows, and this course provides the essential strategies needed to operate them effectively in production. You will explore the core principles of LLMOps, understanding why reliable deployment, monitoring, and continuous improvement are critical in today’s AI-driven landscape. Through practical explanations and hands-on guidance, the course helps you build confidence in optimizing LLM performance, managing model lifecycles, and applying scalable operational techniques. By learning how to streamline workflows and apply governance best practices, you will gain the skills needed to deliver consistent, secure, and high-quality AI outcomes. The content blends foundational theory with real-world practices, offering a balanced view of both the technical and operational challenges in modern LLM systems. Concepts are reinforced through practical frameworks and actionable strategies to ensure meaningful application in professional environments. This course is ideal for machine learning engineers, data science practitioners, AI leaders, and technical professionals aiming to enhance their expertise in deploying and managing LLMs. Foundational knowledge of machine learning is recommended to maximize learning outcomes.

Навыки, которые вы освоите

Model OptimizationLarge Language ModelingModel EvaluationResponsible AIScalabilityContinuous MonitoringModel DeploymentArtificial IntelligenceArtificial Intelligence and Machine Learning (AI/ML)AI WorkflowsMLOps (Machine Learning Operations)LLM ApplicationGenerative AIData CollectionData ProcessingModel Training

Программа курса

8 модулей · 70 учебных материалов

01Introduction to LLMs and LLMOps10 материалов

Lesson 1

Course OverviewВидеоIntroduction to LLMs and LLMOps - Overview VideoВидеоIntroductionЧтениеDeep Learning RevolutionЧтение

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Essential Guide to LLMOps
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 12 ч

8 модулей

Язык: Английский

Часть программы вашего университета
Current State and Future DirectionsЧтение
EducationЧтение
Challenges and Innovations in Scaling Up LLM ArchitecturesЧтение
Pre-training and Fine-tuningЧтение
Foundations of Large Language Models and OperationsЗадание
Discussion: From MLOps to LLMOpsОбсуждение
02Reviewing LLMOps Components8 материалов

Lesson 1

Reviewing LLMOps Components - Overview VideoВидеоIntroductionЧтениеDataset Storage and Database Management Systems (DBMSs)ЧтениеSliding Window NuancesЧтениеAccess ControlЧтениеPractice: Design an LLMOps PlanDIALOGUERegulatory ComplianceЧтениеLLMOps Fundamentals ReviewЗадание
03Processing Data in LLMOps Tools7 материалов

Lesson 1

Processing Data in LLMOps Tools - Overview VideoВидеоIntroductionЧтениеCollecting Unstructured DataЧтениеPreparing DataЧтениеReflect: Explore Data Pipeline Trade-offsDIALOGUEAutomating Spark JobsЧтениеData Processing Fundamentals in LLMOpsЗадание
04Developing Models via LLMOps9 материалов

Lesson 1

Developing Models via LLMOps - Overview VideoВидеоIntroductionЧтениеUniquely Identifying Tokens with Attention MasksЧтениеRetrieving FeaturesЧтениеPractice: Diagnose a Failing LLM SystemDIALOGUEFine-tuning the Foundation LLMЧтениеTuning HyperparametersЧтениеLLMOps Fundamentals and Model DevelopmentЗаданиеDiscussion: Balancing Fine-Tuning, Hyperparameter Optimization, and AutomationОбсуждение
05LLMOps Review and Compliance8 материалов

Lesson 1

LLMOps Review and Compliance - Overview VideoВидеоIntroductionЧтениеEvaluating Perplexity, BLEU, and ROUGEЧтениеEvaluating Conversational FlowЧтениеPractice: Design a Hybrid LLM Evaluation PlanDIALOGUEManaging OWASP Risks in LLMsЧтениеEnsuring Legal and Regulatory ComplianceЧтениеLLMOps Compliance and Governance EssentialsЗадание
06LLMOps Strategies for Inference, Serving, and Scalability10 материалов

Lesson 1

LLMOps Strategies for Inference, Serving, and Scalability - Overview VideoВидеоIntroductionЧтениеModel QuantizationЧтениеTrade-offs Between Inference Speed and Output QualityЧтениеPractice: Design an LLM Deployment StrategyDIALOGUELeveraging the Microservices ArchitectureЧтениеServing Up-to-Date ModelsЧтениеIncreasing Model ReliabilityЧтениеLLMOps Strategies and System DesignЗаданиеDiscussion: Balancing Inference Speed and Model ServingОбсуждение
07LLMOps Monitoring and Continuous Improvement10 материалов

Lesson 1

LLMOps Monitoring and Continuous Improvement - Overview VideoВидеоIntroductionЧтениеFostering Trust and ReliabilityЧтениеCustom SolutionsЧтениеLearning from Human FeedbackЧтениеChallenges in Integrating FeedbackЧтениеPractice: Design an LLM Monitoring & Improvement LoopDIALOGUEIncorporating Continuous ImprovementЧтениеMetrics Used and Performance Improvements ObservedЧтениеLLMOps Monitoring and Continuous Improvement FundamentalsЗадание
08The Future of LLMOps and Emerging Technologies8 материалов

Lesson 1

The Future of LLMOps and Emerging Technologies - Overview VideoВидеоIntroductionЧтениеIntegration of Multimodal CapabilitiesЧтениеChallenges and LimitationsЧтениеAI and IoT ConvergenceЧтениеPractice: Architecting a Real-World AI & IoT SystemDIALOGUEScalability and FlexibilityЧтениеExploring the Future of AI Operations and TechnologiesЗадание