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

Cloud StorageAI for Business FoundationsBusiness IntelligenceBusiness StrategyArtificial Intelligence FoundationsArtificial Intelligence for BusinessData AnalysisArtificial Intelligence (AI)Cloud ComputingData ScienceBusiness Analysis and StrategyLeadership and ManagementBusiness Software and ToolsOne-Off

Concepts

0. Introduction

01 - Welcome to this course
02 - Things you should know

1. Data Collection Fundamentals

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

2. Data Quality Frameworks

09 - Data quality metrics
10 - Data validation and verification procedures
11 - Error detection and correction methodologies
12 - Challenge - Data quality assessment
13 - Solution - Data quality assessment

3. Data Preparation

14 - Data normalization and standardization
15 - Handling missing data and outliers
16 - Data augmentation techniques
17 - Feature engineering and selection
18 - Challenge - Engineer features
19 - Solution - Engineer features

4. AI Storage Systems

20 - Types of storage systems - Object storage
21 - Types of storage systems - Block and file storage
22 - Types of storage systems - Databases
23 - Challenge - Storage system selection
24 - Solution - Storage system selection

5. AI Processing Infrastructure

25 - Batch processing systems
26 - Stream processing systems
27 - Scaling AI processing
28 - Challenge - Choosing a framework
29 - Solution - Choosing a framework

6. Optimizing AI Process Workflows

30 - Metadata management
31 - Feature stores
32 - Caching
33 - Challenge - Feature store capabilities
34 - Solution - Feature store capabilities

7. Real-Time Data Processing for AI Applications

35 - Event-driven architectures
36 - Real-time data integration patterns
37 - Monitoring and alerting systems
38 - Challenge - Architecture patterns
39 - Solution - Architecture patterns

8. Secure Data Management for AI Implementation

40 - Security compliance and regulation
41 - Privacy-preserving AI methods
42 - Data governance frameworks
43 - Audit trails and logging
44 - Challenge - Privacy-preserving methods
45 - Solution - Privacy-preserving methods

9. Ethical Considerations

46 - AI ethics and responsible data use
47 - Transparency and explainability
48 - Ethical guidelines and standards
49 - Challenge - Ethical principles
50 - Solution - Ethical principles

Conclusion

51 - Next steps