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LLM Engineer’s Handbook · LearnSpace
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LLM Engineer’s Handbook

Курс от Packt
Начальный≈ 23.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

In this comprehensive course, you will explore the intricate world of Large Language Models (LLMs) and gain the skills to design, train, and deploy them using cutting-edge MLOps practices. LLMs are revolutionizing the AI landscape, and understanding how to develop and manage them is essential for AI professionals. This course is designed to help you not only grasp the core concepts behind LLMs but also give you hands-on experience to build production-grade LLM systems. You'll learn how to create scalable, efficient LLM systems from scratch, focusing on real-world applications that will make you stand out in the AI industry. What sets this course apart is its combination of in-depth theoretical insights and real-world, practical applications. You'll move beyond basic knowledge to master LLM architecture, supervised fine-tuning, and deployment on cloud platforms, ensuring that you’re fully equipped to build robust, production-ready systems. This course is ideal for AI engineers, NLP professionals, and anyone looking to deepen their expertise in LLM engineering. A basic understanding of LLMs, Python, and cloud platforms like AWS is recommended for optimal learning.

Навыки, которые вы освоите

Retrieval-Augmented GenerationModel OptimizationFine-tuningModel DeploymentData PipelinesMLOps (Machine Learning Operations)Extract, Transform, LoadModel EvaluationLLM ApplicationLarge Language ModelingCI/CDData Processing

Программа курса

11 модулей · 81 учебных материалов

01Understanding the LLM Twin Concept and Architecture6 материалов

Lesson 1

Course OverviewВидеоUnderstanding the LLM Twin Concept and Architecture - Overview VideoВидеоIntroductionЧтениеBuilding ML systems with feature/training/inference pipelinesЧтение

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Packt - Course Instructors

Преподаватель курса

LLM Engineer’s Handbook
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

≈ 23.7 ч

11 модулей

Язык: Английский

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

Часть программы вашего университета
Designing the System Architecture of the LLM TwinЧтение
Designing an LLM-Based SystemЗадание
02Tooling and Installation4 материалов

Lesson 1

Tooling and Installation - Overview VideoВидеоIntroductionЧтениеZenML: Orchestrator, Artifacts, and MetadataЧтениеMLOps and LLMOps ConceptsЗадание
03Data Engineering6 материалов

Lesson 1

Data Engineering - Overview VideoВидеоIntroductionЧтениеZenML Pipeline and StepsЧтениеThe CrawlersЧтениеThe ORM and ODM Software PatternsЧтениеDesigning Data Collection PipelinesЗадание
04RAG Feature Pipeline10 материалов

Lesson 1

RAG Feature Pipeline - Overview VideoВидеоIntroductionЧтениеWhat are Embeddings?ЧтениеDB OperationsЧтениеPractice: Diagnose and Optimize a RAG SystemDIALOGUEExploring the LLM Twin’s RAG Feature Pipeline ArchitectureЧтениеChange data capture: syncing the data warehouse and feature storeЧтениеQuerying the Data WarehouseЧтениеOVMЧтениеAdvanced Concepts in Retrieval-Augmented Generation (RAG)Задание
05Supervised Fine-Tuning10 материалов

Lesson 1

Supervised Fine-Tuning - Overview VideoВидеоIntroductionЧтениеData DeduplicationЧтениеData GenerationЧтениеPractice: Design a Data Strategy for SFTDIALOGUECreating Our Own Instruction DatasetЧтениеExploring SFT and its TechniquesЧтениеTraining ParametersЧтениеFine-tuning in PracticeЧтениеAdvanced Techniques in Language Model Fine-TuningЗадание
06Fine-Tuning with Preference Alignment7 материалов

Lesson 1

Fine-Tuning with Preference Alignment - Overview VideoВидеоIntroductionЧтениеEvaluating PreferencesЧтениеPractice: Design an AI Alignment StrategyDIALOGUEPreference AlignmentЧтениеImplementing DPOЧтениеUnderstanding Preference Alignment in AI SystemsЗадание
07Evaluating LLMs6 материалов

Lesson 1

Evaluating LLMs - Overview VideoВидеоIntroductionЧтениеTask-specific LLM EvaluationsЧтениеPractice: Design an LLM Evaluation PlanDIALOGUEGenerating answersЧтениеAdvanced Evaluation Techniques for LLM SystemsЗадание
08Inference Optimization6 материалов

Lesson 1

Inference Optimization - Overview VideoВидеоIntroductionЧтениеOptimized Attention MechanismsЧтениеPractice: Design an LLM Optimization StrategyDIALOGUEIntroduction to QuantizationЧтениеOptimizing Large Language Model InferenceЗадание
09RAG Inference Pipeline8 материалов

Lesson 1

RAG Inference Pipeline - Overview VideoВидеоIntroductionЧтениеSelf-queryingЧтениеAdvanced RAG Post-retrieval Optimization: RerankingЧтениеPractice: Diagnose and Optimize a RAG PipelineDIALOGUEImplementing the LLM Twin's RAG Inference PipelineЧтениеBringing Everything Together into the RAG Inference PipelineЧтениеAdvanced RAG Pipeline ImplementationЗадание
10Inference Pipeline Deployment8 материалов

Lesson 1

Inference Pipeline Deployment - Overview VideoВидеоIntroductionЧтениеMonolithic versus Microservices Architecture in Model ServingЧтениеPractice: Choosing an LLM Deployment ArchitectureDIALOGUEExploring the LLM Twin’s Inference Pipeline Deployment StrategyЧтениеDeploying the LLM Twin model to AWS SageMakerЧтениеCalling the AWS SageMaker Inference EndpointЧтениеModern ML Model DeploymentЗадание
11MLOps and LLMOps10 материалов

Lesson 1

MLOps and LLMOps - Overview VideoВидеоIntroductionЧтениеMLOps PrinciplesЧтениеPrompt MonitoringЧтениеPractice: Design an End-to-End LLMOps PipelineDIALOGUESetting up the ZenML CloudЧтениеRun the Pipelines on AWSЧтениеGitHub Actions CI YAML FileЧтениеTrigger downstream pipelinesЧтениеMLOps and LLMOps FundamentalsЗадание