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Production-Ready Multimodal ML Engineering · LearnSpace
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Production-Ready Multimodal ML Engineering

Курс от Coursera
Средний≈ 15.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Production machine learning systems don't run on model accuracy alone — they depend on reliable data pipelines, optimized inference, and scalable cloud infrastructure. This course integrates the full stack of ML engineering skills needed to build and operate multimodal AI systems in the real world. You will design a unified feature store schema for image, audio, and text data, then automate ingestion and validation using Apache Airflow and Great Expectations. You will apply test-driven development to PyTorch data loaders and training loops, optimize a model for real-time inference using TensorRT, and manage your codebase with GitFlow and CI/CD pipelines. Finally, you will containerize and deploy a GPU-accelerated service to Kubernetes, tuning autoscaling to meet production performance targets. By the end, you will have a portfolio-ready project demonstrating end-to-end ML infrastructure skills — exactly what employers look for in ML Infrastructure Engineers, MLOps Engineers, and senior ML practitioners.

Навыки, которые вы освоите

Data PipelinesApache AirflowKubernetesExtract, Transform, LoadContainerizationTest Driven Development (TDD)Data ValidationModel TrainingMLOps (Machine Learning Operations)Machine Learning SoftwareNatural Language ProcessingModel DeploymentGenerative AIArtificial IntelligenceMachine Learning AlgorithmsArtificial Intelligence and Machine Learning (AI/ML)Data InfrastructureArtificial Neural NetworksAlgorithmsModel Optimization

Программа курса

14 модулей · 91 учебных материалов

01 Create Unified Data Schema for Multimodal ML Features7 материалов
Why Unified Schemas Matter for Multimodal AI SuccessВидеоFundamentals of Multimodal Data Schema ArchitectureВидеоBigQuery Schema Design Patterns for Multimodal FeaturesЧтениеBuilding Your First Multimodal Schema in BigQueryВидео

Учитесь у экспертов

Professionals from the Industry

Преподаватель курса

Production-Ready Multimodal ML Engineering
В каталоге вашей программы

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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 15.2 ч

14 модулей

Язык: Английский

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

Часть программы вашего университета
Design a Production-Ready Multimodal SchemaЗадание
Optimizing Schema Design for Production Scale DIALOGUE
Multimodal Schema Design Knowledge CheckЗадание
02 Implement Automated ETL Pipelines with Workflow Orchestration7 материалов
Navigating ETL Pipeline Architecture DecisionsDIALOGUEApache Airflow Fundamentals for Multimodal Data ProcessingВидеоProduction ETL Patterns for Multimodal Data ProcessingЧтениеCreating Your First Airflow DAG for Multimodal ProcessingВидеоBuild Production-Ready Airflow DAGs for Multimodal Data ProcessingЛабораторнаяETL Pipeline Implementation Knowledge Check ЗаданиеMultimodal ETL Pipeline Implementation AssessmentЗадание
03Understanding Multimodal Data Validation6 материалов
Why Multimodal Data Validation Matters in Production AI SystemsВидеоCore Principles of Multimodal Data ValidationВидеоMultimodal Data Quality Challenges and SolutionsЧтениеIdentifying Data Quality Issues in Multimodal DatasetsВидеоAnalyzing Multimodal Data Quality IssuesDIALOGUEMultimodal Data Validation Fundamentals AssessmentЗадание
04 Implementing Validation Frameworks7 материалов
Production Validation Framework SelectionDIALOGUEGreat Expectations Framework for Multimodal ValidationЧтениеSetting Up Great Expectations for Multimodal Data ValidationВидеоBuilding Automated Multimodal Validation PipelinesВидеоImplementing Multimodal Data Validation FrameworkЛабораторнаяImplementing Validation Frameworks AssessmentЗаданиеMultimodal Data Validation Mastery AssessmentЗадание
05 Foundation - TDD Principles & ML Code Architecture6 материалов
Why Production-Quality ML Code Matters ВидеоTest-Driven Development Fundamentals for ML ComponentsВидеоModular Architecture Patterns for ML SystemsЧтениеImplementing Basic TDD Workflow for ML ComponentsВидеоApplying TDD Principles to Real-World ML ScenariosDIALOGUETDD and Modular Architecture Knowledge CheckЗадание
06Implementation - DataLoader & Training Loop Development7 материалов
Why Implementation Excellence Drives ML SuccessDIALOGUEProduction ML Implementation Patterns and Best PracticesЧтениеDataLoader and Training Loop ImplementationВидеоImplementing Training Loop Components with Comprehensive TestingВидеоBuild Production-Ready DataLoader and Training Loop with TDDЛабораторнаяProduction ML Implementation Knowledge CheckЗаданиеApply Test-Driven ML Code - Final AssessmentЗадание
07Analyze inference code to optimize for real-time performance6 материалов
Why Real-Time ML Performance Matters in ProductionВидеоProfiling and Bottleneck Identification in ML Inference PipelinesВидеоAdvanced Optimization Techniques: Quantization, Pruning, and Hardware AccelerationЧтениеPodcast: Converting PyTorch Models to TensorRT for Real-Time InferenceЧтениеOptimize ML Inference Pipeline for Real-Time Performance RequirementsDIALOGUEML Inference Optimization Knowledge CheckЗадание
08Evaluate Git branching strategies and CI/CD pipelines for codebase management7 материалов
Choosing the Right Git Branching Strategy for Your ML TeamDIALOGUEGitFlow vs Trunk-Based Development: Comparing ML Development WorkflowsВидеоDesigning CI/CD Pipelines for ML Development: Automated Testing and Deployment StrategiesЧтениеSetting Up GitFlow Workflow with Automated Testing IntegrationЧтениеImplementing GitFlow CI/CD Pipeline for ML TeamsЛабораторнаяGit Branching and CI/CD Pipeline Knowledge CheckЗаданиеML Codebase Management Mastery AssessmentЗадание
09GPU Cluster Configuration for Distributed Training7 материалов
The Strategic Value of Distributed GPU TrainingВидеоCore Concepts of GPU Cluster ArchitectureВидеоComparing AWS, Google Cloud, and Azure GPU OfferingsЧтениеConfiguring Multi-Node Distributed Training with Docker ComposeВидеоPlanning Your Distributed Training StrategyDIALOGUEImplementing Multi-Node PyTorch Distributed TrainingЗаданиеGPU Cluster Configuration Knowledge CheckЗадание
10Containerization and Orchestration Implementation7 материалов
Navigating Production ML Deployment ScenariosDIALOGUEDocker Essentials for Machine Learning DeploymentsЧтениеContainer Orchestration with Kubernetes for ML WorkloadsВидеоEnd-to-End Containerized ML Application DeploymentВидеоComplete Container Orchestration for ML Production SystemsЗаданиеContainerization and Orchestration Knowledge CheckЗаданиеGPU Clusters & Containers - Final AssessmentЗадание
11 Resource Utilization Analysis and Scaling Foundations6 материалов
Why Resource Optimization Matters in Production ML WorkloadsВидеоKubernetes Resource Metrics and Utilization FundamentalsЧтениеDashboard Analysis Techniques for Resource OptimizationВидеоAnalyzing Resource Utilization Patterns in GrafanaВидеоResource Utilization Analysis and Optimization RecommendationsЗаданиеResource Utilization Analysis Knowledge CheckЗадание
12 Advanced Scaling Optimization and Assessment7 материалов
Comprehensive Kubernetes Scaling Strategy ImplementationDIALOGUEHorizontal Pod Autoscaler Configuration and OptimizationЧтениеResource Requests, Limits, and Cost Optimization StrategiesВидеоConfiguring Horizontal Pod Autoscalers for ML WorkloadsВидеоComprehensive Kubernetes Scaling Strategy ImplementationЗаданиеKubernetes Scaling and Resource Optimization AssessmentЗаданиеKubernetes Resource Optimization Mastery AssessmentЗадание
13Project: Production-Ready Multimodal ML Engineering5 материалов
Why This Project MattersЧтениеProject RequirementsЧтениеAssignment: Production-Ready Multimodal ML SystemЧтениеGraded Quiz: Production-Ready Multimodal ML System ЗаданиеSolution KeyЧтение
14GenAI: GenAI-Enhanced Multimodal ML Engineering6 материалов
Why GenAI Tools Matter for Production ML EngineeringЧтениеGenAI Tools for Multimodal ML WorkflowsЧтениеImplementing GenAI-Assisted ETL Pipelines for Multimodal DataЧтениеDeciding When to Use GenAI for ML Code OptimizationDIALOGUESynthesizing GenAI Integration in Your ML OperationsDIALOGUEKnowledge Check: GenAI-Enhanced Multimodal ML EngineeringЗадание