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End-to-End Multimodal AI: Fine-Tuning, Fusion, and MLOps · LearnSpace
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End-to-End Multimodal AI: Fine-Tuning, Fusion, and MLOps

Курс от Coursera
Средний≈ 21.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Build production-ready multimodal AI systems that combine vision, language, and audio into unified intelligent applications. This course takes you through the full lifecycle of multimodal model development — from constructing and fine-tuning transformer-based architectures using PyTorch and TensorFlow, to diagnosing training failures, designing cross-modal retrieval systems, and deploying secure, monitored inference APIs. You will work with real-world tools including CLIP, ViT, FAISS, FastAPI, MLflow, and Ray Tune to build systems that process and integrate multiple data types simultaneously. You will analyze computational complexity to optimize fusion algorithms, evaluate model errors to identify failure patterns, and translate model outputs into stakeholder-ready business insights. This course is built for intermediate practitioners in machine learning and AI who want to move beyond single-modality models and into the cutting edge of AI systems design. By the end, you will have a portfolio of deployable, optimized multimodal systems that demonstrate advanced engineering capability to employers.

Навыки, которые вы освоите

API DesignModel OptimizationMLOps (Machine Learning Operations)Model TrainingTransfer LearningFine-tuningData ScienceData ArchitectureOAuthMachine Learning SoftwareVision Transformer (ViT)Machine Learning AlgorithmsRestful APISolution ArchitectureModel EvaluationTechnical CommunicationApplication Programming Interface (API)Model DeploymentArtificial Intelligence and Machine Learning (AI/ML)Machine Learning

Программа курса

20 модулей · 123 учебных материалов

01 MLOps Foundations for Multimodal AI Systems7 материалов
Why Modular Data Pipelines Matter in Enterprise EnvironmentsВидеоFundamentals of Modular Data Pipeline ArchitectureЧтениеOpen Source Tools for Pipeline Development: Spark, dbt, and AirflowВидеоChoosing the Right Tools for Your Pipeline ArchitectureDIALOGUE

Учитесь у экспертов

Professionals from the Industry

Преподаватель курса

End-to-End Multimodal AI: Fine-Tuning, Fusion, and MLOps
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 21.2 ч

20 модулей

Язык: Английский

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

Часть программы вашего университета
Fine-tuning Multimodal TransformersВидео
Building Your First Modular Pipeline ComponentЛабораторная
Modular Pipeline Foundations Knowledge CheckЗадание
02Transfer Learning, Data Transformation, and Model Delivery Pipelines6 материалов
Solving Real-World Pipeline Challenges Through Modular DesignDIALOGUEAdvanced Pipeline Components: Transformation and Loading StrategiesЧтениеTransfer Learning Acceleration ВидеоTransfer Learning to Accelerate Machine Learning ModelЗаданиеModular Pipeline Design AssessmentЗаданиеModular Data Pipeline Mastery AssessmentЗадание
03Diagnosing Training Dynamics Issues6 материалов
When Neural Networks Fail: The Hidden Cost of Training ProblemsВидеоUnderstanding Training Dynamics: Patterns, Gradients, and Warning SignsВидеоMathematical Foundations of Gradient AnalysisЧтениеAnalyzing Training Curves and Gradient PatternsDIALOGUENeural Network Training Diagnostics LabЛабораторнаяTraining Dynamics Diagnosis AssessmentЗадание
04Implementing Training Stabilization Interventions6 материалов
The Cost of Reactive vs Proactive Training ManagementDIALOGUETraining Stabilization Techniques: Gradient Clipping and Early StoppingЧтениеImplementing Gradient Clipping in TensorFlow and PyTorchВидеоTraining Pipeline Stabilization ImplementationЗаданиеTraining Stabilization Techniques AssessmentЗаданиеFinal Assessment: Neural Network Training StabilizationЗадание
05Image Preprocessing and Normalization7 материалов
Why Image Preprocessing Matters in Computer VisionВидеоFundamentals of Image Normalization and Color Space TheoryЧтениеImplementing Normalization Techniques with NumPyВидеоConverting Between Color Spaces with OpenCVВидеоImage Preprocessing Pipeline: Normalization & Color-Space TransformationsЛабораторнаяNavigating Image Preprocessing Decisions for Computer Vision PipelinesDIALOGUEImage Preprocessing Fundamentals AssessmentЗадание
06Motion Feature Extraction6 материалов
Real-World Motion Analysis ApplicationsDIALOGUEOptical Flow Theory and Frame Differencing FundamentalsЧтениеImplementing Optical Flow with OpenCVВидеоHands-On Frame Differencing ImplementationВидеоMotion Feature Extraction AssessmentЗаданиеMotion Detection using Optical Flow and Frame Differencing - Final AssessmentЗадание
07Error Analysis Foundations6 материалов
Why Systematic Error Analysis Matters in Computer VisionВидеоFoundations of Computer Vision Error AnalysisЧтениеUnderstanding Confusion Matrices and Error CategoriesВидеоAnalyzing Confusion Matrix PatternsDIALOGUEHands-On Confusion Matrix Analysis for Computer Vision ModelsЛабораторнаяEvaluating Error Analysis FundamentalsЗадание
08Systematic Failure Pattern Identification6 материалов
Real-World Impact of Systematic Failure AnalysisDIALOGUEAdvanced Error Pattern Recognition TechniquesЧтениеImplementing Visual Error Analysis and Pattern RecognitionВидеоComprehensive Failure Pattern Analysis ProjectЗаданиеAdvanced Failure Pattern Recognition AssessmentЗаданиеComprehensive Error Analysis Mastery AssessmentЗадание
09ANN Cross-Modal Search - Foundation6 материалов
Architecting Cross-Modal Intelligence: A Strategic DialogueDIALOGUEFundamentals of Cross-Modal Retrieval SystemsВидеоFAISS Architecture and Index Types for Production SystemsЧтениеImplementing FAISS Indexing for Cross-Modal SearchЧтениеBuilding Production-Scale Cross-Modal Retrieval with FAISSЛабораторнаяCross-Modal Retrieval and FAISS Implementation AssessmentЗадание
10Attention-Based Fusion - Application & Assessment6 материалов
Strategic Decision-Making for Multimodal Fusion ArchitectureDIALOGUEArchitecture and Mathematics of Attention-Based Multimodal FusionЧтениеImplementing Cross-Modal Attention MechanismsЧтениеOptimizing Attention Fusion for Production DeploymentЗаданиеAttention-Based Fusion Architecture AssessmentЗаданиеCross-Modal Retrieval and Attention-Based Fusion Mastery AssessmentЗадание
11Foundation - Complexity Analysis Fundamentals7 материалов
Why Algorithm Complexity Analysis Matters in Production AIВидеоFundamentals of Computational Complexity in Fusion AlgorithmsЧтениеApplying Big O Analysis to Fusion Algorithm ComponentsВидеоProfiling Fusion Algorithms with cProfileВидеоWhy Algorithm Complexity Analysis Matters in Production AIDIALOGUEProfile and Analyze Fusion Algorithm PerformanceЛабораторнаяComplexity Analysis Fundamentals AssessmentЗадание
12Core Application - Algorithm Optimization & Trade-offs5 материалов
Strategic Optimization Decision MakingDIALOGUEImplementing Sparse Attention OptimizationВидеоOptimization Strategy Evaluation and ImplementationЗаданиеAlgorithm Optimization and Trade-offs AssessmentЗаданиеComprehensive Fusion Algorithm Analysis AssessmentЗадание
13Production Model Performance Evaluation and Drift Detection5 материалов
The Critical Business Impact of Undetected Model DriftDIALOGUEUnderstanding Model Drift Types and Detection MethodsЧтениеImplementing Drift Detection with Statistical MonitoringВидеоBuilding Production Drift Monitoring SystemsЛабораторнаяProduction Model Monitoring AssessmentЗадание
14Automated ML Pipeline Creation and Optimization7 материалов
The Business Impact of Manual ML OperationsDIALOGUEEnd-to-End ML Pipeline Architecture and ComponentsВидеоHyperparameter Optimization Strategies and Integration PatternsЧтениеBuilding Automated ML Pipelines with Ray Tune and MLflowВидеоEnterprise ML Pipeline ImplementationЗаданиеAutomated ML Pipeline Mastery AssessmentЗаданиеFinal Course Assessment - Automated ML OperationsЗадание
15Multimodal Model Analysis Fundamentals6 материалов
The Business Impact of Multimodal AI InterpretationВидеоUnderstanding Multimodal AI Model Architecture and Output PatternsЧтениеExplainability Tools and Techniques for Multimodal AnalysisВидеоImplementing Grad-CAM Analysis for Multimodal Model InterpretationDIALOGUEMultimodal AI Model Analysis for Business StakeholdersЛабораторнаяMultimodal Analysis Fundamentals Knowledge CheckЗадание
16Stakeholder Communication & Insight Delivery7 материалов
When Technical Excellence Isn't Enough: The Communication Gap in AIВидеоBusiness Narrative Frameworks for AI InsightsЧтениеCreating Executive Briefings from Technical AI AnalysisВидеоBuilding Stakeholder Presentations That Drive ActionDIALOGUEDeveloping Comprehensive Executive Briefing from Multimodal AnalysisЗаданиеStakeholder Communication Fundamentals Knowledge CheckЗаданиеComprehensive Multimodal AI Analysis and Stakeholder Communication AssessmentЗадание
17API Endpoint Design for Multimodal Inference6 материалов
Why API Versioning Matters for Multimodal AI ServicesВидеоFundamentals of Multimodal API Endpoint DesignВидеоDesigning Robust Data Contracts for Multimodal InputsЧтениеImplementing Versioned Endpoints with FastAPIВидеоBuild a Versioned Multimodal API PrototypeЗаданиеAPI Endpoint Design Knowledge CheckЗадание
18 Security & Monitoring Middleware Implementation6 материалов
Why Security and Monitoring Are Critical for Production APIsDIALOGUEOAuth2 Authentication and API Security FundamentalsВидеоImplementing Comprehensive API Monitoring and ObservabilityЧтениеImplementing OAuth2 Security Middleware with FastAPIВидеоBuild Comprehensive Security and Monitoring MiddlewareЗаданиеSecurity and Monitoring Implementation Knowledge CheckЗадание
19OpenAPI Documentation & Specification7 материалов
Why Comprehensive API Documentation Drives Developer AdoptionВидеоOpenAPI Specification Design for Developer IntegrationЧтениеAdvanced OpenAPI Features for Multimodal APIsВидео Implementing OpenAPI Documentation for Production APIsDIALOGUEOpenAPI Specification for Multimodal AI ServicesЛабораторнаяOpenAPI Documentation Knowledge CheckЗаданиеComprehensive OpenAPI Documentation AssessmentЗадание
20Project: End-to-End Multimodal AI: Fine-Tuning, Fusion, and MLOps5 материалов
Why This Project MattersЧтениеProject RequirementsЧтениеAssignment: Multimodal AI System ImplementationЧтениеGraded Quiz: Multimodal AI System Implementation ЗаданиеSolution KeyЧтение