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
Optimizing, Deploying, and Governing LLMs in the Enterprise · LearnSpace
Назад в каталог
courseraIT и технологии

Optimizing, Deploying, and Governing LLMs in the Enterprise

Курс от Packt
Продвинутый≈ 8.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Master strategies for data management, deployment, monitoring, and responsible AI in large language model operations. Stay ahead with insights into emerging trends and multimodal applications in enterprise environments. This course equips learners with advanced skills for managing the full lifecycle of LLMs in production, from crafting effective data strategies and optimizing inferencing to deploying at scale and ensuring robust monitoring. Learners will explore best practices for responsible AI, addressing ethical and regulatory considerations while exploring the latest trends in multimodal LLMs. By the end of the course, learners will be prepared to lead enterprise LLM initiatives with a focus on performance, compliance, and innovation. The course takes learners through real-world case studies, videos, and knowledge checks to gain practical expertise in deploying, optimizing, and governing LLMs. These materials foster a forward-looking perspective, enabling professionals to navigate the evolving landscape of enterprise AI. With a structured approach, you'll master everything from the data blueprint to managing the deployment and monitoring of models in production. Designed for professionals in AI, data science, and enterprise technology, the course is perfect for those who want to gain expertise in deploying LLMs at scale. Ideal for enterprise leaders, AI practitioners, and developers, the course is suitable for learners with some experience in AI or data science. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. By the end of the course, you will be able to manage LLM lifecycles effectively, deploy models at scale, optimize inferencing, monitor LLMs in production, implement responsible AI practices, and stay ahead of emerging trends.

Навыки, которые вы освоите

Model OptimizationMultimodal PromptsLarge Language ModelingLLM ApplicationResponsible AIData ManagementMLOps (Machine Learning Operations)Model DeploymentData EthicsAI IntegrationsPerformance TuningData StrategyAI PersonalizationData GovernanceScalabilityAI WorkflowsGenerative AIEnterprise Application ManagementData Preprocessing

Программа курса

7 модулей · 81 учебных материалов

01The Data Blueprint: Crafting Effective Strategies for LLM Development13 материалов

Mastering Data Strategies for Powerful Language Models

OverviewВидеоIntroductionЧтениеImportance of Data in LLM DevelopmentЧтениеData AugmentationЧтение

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

Optimizing, Deploying, and Governing LLMs in the Enterprise
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

≈ 8.1 ч

7 модулей

Язык: Английский

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

Часть программы вашего университета
Data Quality VariabilityЧтение
Case Studies on Effective Data StrategiesЧтение
Example Code Snippet Fine-Tuning DeepSeek for a Classification TaskЧтение
Benefits of Synthetic DataЧтение
Data Annotation and LabelingЧтение
Data PartitioningЧтение
Mitigation StrategiesЧтение
Entity Recognition and LinkingЧтение
Data Strategy and Management in Large Language ModelsЗадание
02Managing Model Deployments in Production10 материалов

Optimizing and Governing LLM Deployments for Real-World Impact

OverviewВидеоIntroductionЧтениеEfficient Model DesignЧтениеEdge ComputingЧтениеCaching MechanismsЧтениеThe expected outputЧтениеMeeting Stricter Business and Regulatory RequirementsЧтениеPerformance AuditsЧтениеStoring Forex Data in ChromaЧтениеModel Deployment FundamentalsЗадание
03Accelerated and Optimized Inferencing Patterns11 материалов

Mastering Efficient LLM Deployment: Techniques, Tools, and Trends

OverviewВидеоIntroductionЧтениеHalf-Precision Floating Point (FP16)ЧтениеDeployment Engines Comparative AnalysisЧтениеUse Cases Scalable Multi-GPU DeploymentsЧтениеModel Compilation and QuantizationЧтениеMulti-framework Support (PyTorch, TensorFlow, and ONNX)ЧтениеCross-platform Deployment (Edge, Cloud, or Mobile)ЧтениеLatency Optimization MLC versus CTranslate2 versus vLLMЧтениеAdvanced Topics and Emerging TrendsЧтениеOptimizing LLM Inference SystemsЗадание
04Connected LLMs Pattern13 материалов

Building Intelligent Networks: Architectures and Innovations in Connected LLM Systems

OverviewВидеоIntroductionЧтениеArchitectures for Connected LLMsЧтениеAutonomous Agents (AutoGPT and BabyAGI)ЧтениеCross-model Knowledge SharingЧтениеKey Enabling TechnologiesЧтениеDSPy for Programmable PipelinesЧтениеReinforcement Learning-Based RoutingЧтениеDistributed Vector Databases for Context PassingЧтениеCost EfficiencyЧтениеAdvanced PatternsЧтениеHybrid Symbolic-LLM SystemsЧтениеExploring Advanced AI System DesignЗадание
05Monitoring LLMs in Production12 материалов

Mastering Production-Ready LLM Operations: Metrics, Reliability, and Scaling

OverviewВидеоIntroductionЧтениеKey Metrics for Monitoring LLMsЧтениеBuilding Reliable and Robust LLM SystemsЧтениеTesting StrategiesЧтениеRedundancy ArchitecturesЧтениеSecuring LLMs Privacy, Threats, and ComplianceЧтениеOptimizing Costs and Scaling DeploymentsЧтениеScaling Architectures and Deployment PatternsЧтениеGlobal Deployment ConsiderationsЧтениеField Insights and the Future of LLM OperationsЧтениеMonitoring and Managing Large Language ModelsЗадание
06Responsible AI in LLMs12 материалов

Navigating Ethics, Fairness, and Safety in Large Language Models

OverviewВидеоIntroductionЧтениеWhy LLMs Pose Unique Ethical ChallengesЧтениеThe Evolving Regulatory Landscape for AIЧтениеFairness by DesignЧтениеEthical Considerations in LLMsЧтениеPost Hoc Calibrated Output FilteringЧтениеReal-time Content Moderation SystemЧтениеDocumenting Model BehaviorЧтениеSafety and Robustness in LLMsЧтениеConstitutional AI ImplementationЧтениеResponsible AI in Large Language ModelsЗадание
07Emerging Trends and Multimodality10 материалов

Unifying Language, Vision, and Beyond: The Rise of Multimodal AI

OverviewВидеоIntroductionЧтениеKey Drivers Data Availability Hardware Advances and User DemandЧтениеText and Video Phenaki and VideoPoetЧтениеCross-modal Attention MechanismsЧтениеContrastive LearningЧтениеTechnological Advances in Multimodal AIЧтениеEfficient Fusion Techniques (Q-Former and Perceiver Resampler)ЧтениеA Multimodal Use Case in the Medical DomainЧтениеExploring Multimodal AI and Its ApplicationsЗадание