03 — Implement data validation and processing pipelines

03 — Implement data validation and processing pipelines

Module Introduction

• Introduction
• Key topics

Input Quality Fundamentals

• Introduction
• Multi-turn dialog formatting fundamentals
• Input quality impact on model outputs
• Consistency and reliability considerations

Introduction To Data Quality For Foundation Models

• Introduction
• Data quality for foundation models
• Common data quality challenges
• Key quality dimensions
• Impact on foundation model performance
• Building a data quality mindset

Advanced Validation Techniques Across Aws Services

• Introduction
• Real-time and custom validation with AWS Lambda
• DQDL implementation in AWS Glue
• Text data validation
• Interactive and automated validation with Data Wrangler
• Integration and monitoring

Integrated Monitoring And Remediation

• Introduction
• Amazon CloudWatch metrics for data quality tracking
• Monitoring for foundation model data validation pipelines
• Automated remediation workflows
• Proactive issue detection and response
• Advanced data quality monitoring techniques
• Amazon Bedrock AgentCore for data quality management
• Integration with remediation systems
• AWS Security Hub for data pipeline security

End-to-end Workflow Integration

• Introduction
• Pipeline architecture patterns
• Designing effective data validation pipeline architecture
• Orchestration with Step Functions
• Amazon Nova Act for automated data workflows
• Data validation pipelines with step functions
• Quality gates and conditional processing
• Feedback loops and continuous improvement
• Designing effective data validation pipeline architecture
• Real-world implementation example
• Enhanced storage capabilities for data pipelines

Json Formatting For Amazon Bedrock Api

• Introduction
• Amazon Bedrock API request structure
• Universal JSON fields
• Model-specific JSON formatting
• Advanced JSON configuration
• Error handling and debugging
• JSON schema validation
• API testing and validation tools
• Best practices for JSON formatting

Structured Data Preparation For Amazon Sagemaker Endpoints

• Introduction
• SageMaker endpoint input requirements
• Data preprocessing pipelines
• Performance optimization strategies

Conversation Formatting For Dialog Applications

• Introduction
• Multi-turn dialog formatting fundamentals
• Model-specific formatting schemas
• Context window management strategies

Text Preprocessing And Normalization For Foundation Models

• Introduction
• Text reformatting with Amazon Bedrock
• Amazon Nova models for text preprocessing
• Text standardization techniques
• Entity extraction with Amazon Comprehend and Amazon Bedrock
• Data normalization with Lambda
• Healthcare data pipeline example
• Custom model development for specialized processing

Introduction To Multimodal Data Processing

• Introduction
• Understanding multimodal data types
• Multimodal data characteristics
• AWS services for multimodal processing
• Amazon Bedrock multimodal models
• Common multimodal use cases
• Systematic framework for implementing multimodal AI solutions
• Processing workflow fundamentals

Advanced Multimodal Processing Techniques

• Introduction
• Bedrock foundation model integration
• Optimizing Amazon Bedrock foundation model integration
• SageMaker custom processing
• Leveraging SageMaker for custom multimodal processing
• Audio-visual processing with Amazon Transcribe
• Amazon Bedrock Multimodal Models
• Optimizing audio-visual content processing with Amazon Transcribe
• Advanced processing patterns
• Orchestrating complex workflows

Module Summary

• Recap and next steps
• Resources