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Gen AI Dev- Implement data validation & processing pipelines · LearnSpace
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Gen AI Dev- Implement data validation & processing pipelines

Курс от Amazon Web Services
Продвинутый≈ 2.2 чАмериканский английский
О курсеНавыкиПрограммаПреподаватели

О курсе

In this module, you will learn how to do the following:Build effective data validation and processing pipelines for foundation models, ensuring optimal AI performance through high-quality inputs.This module focuses on implementing data validation and processing pipelines for foundation models, covering essential techniques to ensure high-quality data inputs for optimal AI model performance. You will gain comprehensive guidance on data quality management, validation workflows, multimodal processi

Навыки, которые вы освоите

Software DevelopmentAI IntegrationsMultimodal PromptsNatural Language ProcessingCloud ComputingArtificial Intelligence and Machine Learning (AI/ML)Python ProgrammingData ProcessingGenerative AIMachine LearningAmazon Web ServicesData GovernanceAPI DesignData QualityPrompt EngineeringJSONData PipelinesData ValidationData PreprocessingArtificial Intelligence

Программа курса

6 модулей · 86 учебных материалов

01Introduction2 материалов

Module Introduction

IntroductionЧтениеKey topicsЧтение
02Data Quality Foundations for AI Models36 материалов

Input Quality Fundamentals

IntroductionЧтениеMulti-turn dialog formatting fundamentalsЧтение

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AWS Instructor

Преподаватель курса

Gen AI Dev- Implement data validation & processing pipelines
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 2.2 ч

6 модулей

Язык: Американский английский

Часть программы вашего университета
Input quality impact on model outputsЧтение
Consistency and reliability considerationsЧтение

Introduction to Data Quality for Foundation Models

IntroductionЧтениеData quality for foundation modelsЧтениеCommon data quality challengesЧтениеKey quality dimensionsЧтениеImpact on foundation model performanceЧтениеBuilding a data quality mindsetЧтение

Advanced Validation Techniques Across AWS Services

IntroductionЧтениеReal-time and custom validation with AWS LambdaЧтениеDQDL implementation in AWS GlueЧтениеText data validationЧтениеInteractive and automated validation with Data WranglerЧтениеIntegration and monitoringЧтение

Integrated Monitoring and Remediation

IntroductionЧтениеAmazon CloudWatch metrics for data quality trackingЧтениеMonitoring for foundation model data validation pipelinesЧтениеAutomated remediation workflowsЧтениеProactive issue detection and responseЧтениеAdvanced data quality monitoring techniquesЧтениеAmazon Bedrock AgentCore for data quality managementЧтениеIntegration with remediation systemsЧтениеAWS Security Hub for data pipeline securityЧтение

End-to-End Workflow Integration

IntroductionЧтениеPipeline architecture patternsЧтениеDesigning effective data validation pipeline architectureЧтениеOrchestration with Step FunctionsЧтениеAmazon Nova Act for automated data workflowsЧтениеData validation pipelines with step functionsЧтениеQuality gates and conditional processingЧтениеFeedback loops and continuous improvementЧтениеDesigning effective data validation pipeline architectureЧтениеReal-world implementation exampleЧтениеEnhanced storage capabilities for data pipelinesЧтение
03Data Formatting for Model Inference17 материалов

JSON Formatting for Amazon Bedrock API

IntroductionЧтениеAmazon Bedrock API request structureЧтениеUniversal JSON fieldsЧтениеModel-specific JSON formattingЧтениеAdvanced JSON configurationЧтениеError handling and debuggingЧтениеJSON schema validationЧтениеAPI testing and validation toolsЧтениеBest practices for JSON formattingЧтение

Structured Data Preparation for Amazon SageMaker Endpoints

IntroductionЧтениеSageMaker endpoint input requirementsЧтениеData preprocessing pipelinesЧтениеPerformance optimization strategiesЧтение

Conversation Formatting for Dialog Applications

IntroductionЧтениеMulti-turn dialog formatting fundamentalsЧтениеModel-specific formatting schemasЧтениеContext window management strategiesЧтение
04Advanced Data Enhancement Techniques8 материалов

Text Preprocessing and Normalization for Foundation Models

IntroductionЧтениеText reformatting with Amazon BedrockЧтениеAmazon Nova models for text preprocessingЧтениеText standardization techniquesЧтениеEntity extraction with Amazon Comprehend and Amazon BedrockЧтениеData normalization with LambdaЧтениеHealthcare data pipeline exampleЧтениеCustom model development for specialized processingЧтение
05Multimodal Data Processing18 материалов

Introduction to Multimodal Data Processing

IntroductionЧтениеUnderstanding multimodal data typesЧтениеMultimodal data characteristicsЧтениеAWS services for multimodal processingЧтениеAmazon Bedrock multimodal modelsЧтениеCommon multimodal use casesЧтениеSystematic framework for implementing multimodal AI solutionsЧтениеProcessing workflow fundamentalsЧтение

Advanced Multimodal Processing Techniques

IntroductionЧтениеBedrock foundation model integrationЧтениеOptimizing Amazon Bedrock foundation model integrationЧтениеSageMaker custom processingЧтениеLeveraging SageMaker for custom multimodal processingЧтениеAudio-visual processing with Amazon TranscribeЧтение
06Conclusion5 материалов

Course Summary

Graded AssessmentЗаданиеRecap and next stepsЧтениеResourcesЧтениеSurveyЧтение

Contact Us

We want to hear from youЧтение
Amazon Bedrock Multimodal ModelsЧтение
Optimizing audio-visual content processing with Amazon TranscribeЧтение
Advanced processing patternsЧтение
Orchestrating complex workflowsЧтение