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Model Deployment and Monitoring · LearnSpace
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Model Deployment and Monitoring

Курс от Edureka
Средний≈ 7.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course provides an advanced exploration of MLOps, focusing on how enterprise machine learning systems are scaled, governed, optimized, monitored, and continuously improved in production. Through hands-on demonstrations and practical exercises, learners will use Amazon SageMaker, SageMaker Feature Store, SageMaker Clarify, SHAP, LIME, distributed training, hyperparameter tuning, monitoring dashboards, and active learning techniques to create dependable production workflows. By the end of this course, learners will be able to: - Compare managed ML platforms with self-managed infrastructure - Design scalable feature stores and distributed training processes - Implement model approval, governance, bias, and fairness checks - Monitor model performance, data quality, and drift - Apply continuous learning and cost optimization strategies This course is designed for experienced ML engineers, MLOps engineers, AI engineers, platform engineers, and data scientists who want to build and operate enterprise-scale ML systems. Prior knowledge of MLOps, cloud platforms, Kubernetes, model deployment, and monitoring is recommended.

Навыки, которые вы освоите

Distributed ComputingMLOps (Machine Learning Operations)Feature EngineeringSystem MonitoringAWS SageMakerManaged ServicesScalabilityPython ProgrammingScikit Learn (Machine Learning Library)Pandas (Python Package)Artificial IntelligenceMachine Learning AlgorithmsContinuous MonitoringFraud detectionSite Reliability EngineeringAmazon S3Cost ReductionAmazon CloudWatchResponsible AI

Программа курса

4 модулей · 59 учебных материалов

01Managed ML Platforms and Scaling ML Infrastructure20 материалов

Cloud-Native ML Platforms vs. Self-Managed Systems

Specialization OverviewВидеоCourse IntroductionВидеоCourse SyllabusЧтениеCloud Platforms ComparisonВидео

Учитесь у экспертов

Edureka

Преподаватель курса

Model Deployment and Monitoring
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 7.6 ч

4 модулей

Язык: Английский

Часть программы вашего университета
Demonstration: Migrate Fraud Detection from Kubeflow to SageMakerВидео
Demonstration: SageMaker's Integrated MLOps FeaturesВидео
Evaluating Platform Readiness for Enterprise MLOpsЧтение
Practice Quiz: Cloud-Native ML Platforms vs. Self-Managed SystemsЗадание

Managed Feature Stores and Real-Time Feature Serving

SageMaker Feature Store ArchitectureВидеоDemonstration: Set Up SageMaker Feature Store for Fraud DetectionВидеоDemonstration: Feature Versioning and Data Quality MonitoringВидеоDesigning Reliable Feature Stores for Production ML SystemsЧтениеPractice Quiz: Managed Feature Stores and Real-Time Feature ServingЗадание

Automated Training Pipelines and Distributed Training

Distributed Training and HPOВидеоRun Distributed Training with SageMakerВидеоHyperparameter Optimization at ScaleВидеоChoosing the Right Training Strategy for Large-Scale ML WorkloadsЧтениеPractice Quiz: Automated Training Pipelines and Distributed TrainingЗадание

Module Wrap-Up and Assessment

Module Summary: Managed ML Platforms and Scaling ML InfrastructureЧтениеKnowledge Check: Managed ML Platforms and Scaling ML InfrastructureЗадание
02Model Governance, Explainability, and Monitoring in Production18 материалов

Model Governance and Lifecycle Management

Model Governance FrameworksВидеоDemonstration: Implement Model Approval Workflow in SageMakerВидеоDemonstration: Bias and Fairness Detection with SageMakerВидеоBuilding Trustworthy Model Governance Workflows for Production MLЧтениеPractice Quiz: Model Governance and Lifecycle ManagementЗадание

Explainability and Model Debugging in Production

SHAP and LIME for Model ExplainabilityВидеоDemonstration: Generate SHAP Explanations for Fraud PredictionsВидеоDemonstration: Interpret and Export SHAP Explanations for Fraud PredictionsВидеоDemonstration: Debug Model Failures and Root Cause AnalysisВидеоTurning Model Explanations into Actionable Production InsightsЧтениеPractice Quiz: Explainability and Model Debugging in ProductionЗадание

Production Model Monitoring and Data Quality

ML-Specific Monitoring MetricsВидеоDemonstration: Set Up Comprehensive Model Monitoring DashboardВидеоMonitoring Data, Models, and Business Impact TogetherЧтениеPractice Quiz: Production Model Monitoring and Data QualityЗадание

Module Wrap-Up and Assessment

Scaling, Governing, and Monitoring Enterprise ML SystemsDIALOGUEModule Summary: Model Governance, Explainability, and Monitoring in ProductionЧтениеKnowledge Check: Model Governance, Explainability, and Monitoring in ProductionЗадание
03Advanced Production Patterns and Continuous Learning17 материалов

Multi-Model Systems and Model Orchestration

Ensemble and Multi-Model ArchitecturesВидеоDemonstration: Build an Ensemble Fraud Detection SystemВидеоDemonstration: Save and Validate the Ensemble Fraud Detection SystemВидеоDeciding When to Use Ensemble Models in Production MLЧтениеPractice Quiz: Multi-Model Systems and Model OrchestrationЗадание

Online Learning and Continuous Retraining

Online Learning and Active LearningВидеоDemonstration: Implement Incremental Model RetrainingВидеоDemonstration: Active Learning for Cost-Effective LabelingВидеоProgressive Delivery Patterns for ML ModelsЧтениеPractice Quiz: Online Learning and Continuous RetrainingЗадание

Cost Optimization and Operational Excellence

Cost Optimization and SLAsВидеоDemonstration: Cost Analysis and OptimizationВидеоDemonstration: SLOs and Operational DashboardsВидеоInference Optimization Techniques for Production MLЧтениеPractice Quiz: Cost Optimization and Operational ExcellenceЗадание

Module Wrap-Up and Assessment

Module Summary: Advanced Production Patterns and Continuous LearningЧтениеKnowledge Check: Advanced Production Patterns and Continuous LearningЗадание
04Course Wrap-Up and Assessment4 материалов

Course Conclusion

Role Play: Guiding an MLOps Engineer Struggling to Scale Fraud Detection to Enterprise ProductionDIALOGUEPractice Project: MLOps Pipeline – Feature Engineering to ProductionЧтениеEnd Course Knowledge Check: Model Deployment and Monitoring ЗаданиеCourse SummaryВидео