Data Planning, Strategy, and Compliance for AI Initiatives

Course details

Dive into AI strategies that propel organizational goals with expert guidance from cloud architect, author, and Google Cloud expert Dan Sullivan. Learn how to align AI technologies with business objectives. Discover best practices for sourcing data, ensuring data quality, and preserving privacy. Gain insights into structured, semi-structured, and unstructured data, through topics such as batch and stream processing, data governance, audit trails, and ethical frameworks in AI initiatives. Enhance your knowledge in crucial areas such as data integration, regulatory compliance, and feature engineering. Equip yourself with the skills to analyze real-world data challenges and optimize your organization's AI infrastructure. Whether you are tasked with transforming business processes or advancing AI capabilities, this course offers tools that can help you navigate complex data environments and achieve scalable, reliable AI solutions.

Learning objectives
Plan and implement data collection and data quality assessment operations for an AI initiative.
Plan and implement data preparation operations for AI initiatives.
Understand the components of AI processing infrastructure and various ways to optimize that infrastructure.
Know how to assess security considerations and apply security controls to protect the confidentiality, integrity, and availability of data and AI models.

Skills covered

AI Strategy and TransformationAI for Business AnalysisAI Foundations and LiteracyAI for Data Engineers and ScientistsCloud StorageRole-Based AI ApplicationsBusiness IntelligenceBusiness StrategyData AnalysisCloud ComputingData ScienceBusiness Analysis and StrategyLeadership and ManagementBusiness Software and ToolsOne-Off

Concepts

Introduction

Welcome to this course
Things you should know

Data Collection Fundamentals

Identifying data sources
Structured, semi-structured, and unstructured data collection
Data sampling techniques and statistical considerations
Workflows for automated data collection
Challenge - Identify relevant data collection metrics
Solution - Identify relevant data collection metrics

Data Quality Frameworks

Data quality metrics
Data validation and verification procedures
Error detection and correction methodologies
Challenge - Data quality assessment
Solution - Data quality assessment

Data Preparation

Data normalization and standardization
Handling missing data and outliers
Data augmentation techniques
Feature engineering and selection
Challenge - Engineer features
Solution - Engineer features

AI Storage Systems

Types of storage systems - Object storage
Types of storage systems - Block and file storage
Types of storage systems - Databases
Challenge - Storage system selection
Solution - Storage system selection

AI Processing Infrastructure

Batch processing systems
Stream processing systems
Scaling AI processing
Challenge - Choosing a framework
Solution - Choosing a framework

Optimizing AI Process Workflows

Metadata management
Feature stores
Caching
Challenge - Feature store capabilities
Solution - Feature store capabilities

Real-Time Data Processing for AI Applications

Event-driven architectures
Real-time data integration patterns
Monitoring and alerting systems
Challenge - Architecture patterns
Solution - Architecture patterns

Secure Data Management for AI Implementation

Security compliance and regulation
Privacy-preserving AI methods
Data governance frameworks
Audit trails and logging
Challenge - Privacy-preserving methods
Solution - Privacy-preserving methods

Ethical Considerations

AI ethics and responsible data use
Transparency and explainability
Ethical guidelines and standards
Challenge - Ethical principles
Solution - Ethical principles

Conclusion

Next steps