AI Data Strategy: Data Procurement and Storage
1h 52mIntermediate2025-05-08
Authors
Lillian Pierson, P.E.
Engineer, CEO, and Head of Product at Data-Mania
Course details
This course is designed for developers, machine learning engineers, data engineers, data scientists, and cloud professionals who want to master the art of developing data strategy in AI product development. Learn how to effectively source, clean, and manage both structured and unstructured data to optimize machine learning and generative AI models. The course also covers advanced topics such as future-proofing data storage, ensuring compliance, and securing data in AI-driven environments. Through practical lessons and real-world case studies, AI product managers, tech startup founders, and technology executives can also gain valuable insights into how strategic data decisions can drive product success and innovation. Whether you’re building AI products or overseeing their deployment, this course equips you with the essential data skills to thrive in AI-intensive industries.
Learning objectives
Describe the role of data strategy in AI product development, including how strategic data storage and procurement decisions impact the success of AI-driven solutions.
Evaluate various data procurement and storage solutions, assessing their scalability, performance, and cost-effectiveness in the context of AI products.
Execute best practices for ensuring data security, compliance, and future-proofing storage solutions in AI product development.
Analyze real-world case studies to extract valuable lessons on how strategic data decisions can drive successful outcomes in AI product development.
Learning objectives
Describe the role of data strategy in AI product development, including how strategic data storage and procurement decisions impact the success of AI-driven solutions.
Evaluate various data procurement and storage solutions, assessing their scalability, performance, and cost-effectiveness in the context of AI products.
Execute best practices for ensuring data security, compliance, and future-proofing storage solutions in AI product development.
Analyze real-world case studies to extract valuable lessons on how strategic data decisions can drive successful outcomes in AI product development.
Skills covered
Cloud StorageAI for Business FoundationsBusiness StrategyData EngineeringArtificial Intelligence for BusinessCloud ComputingData ScienceBusiness Analysis and StrategyLeadership and ManagementOne-Off
Concepts
0. Introduction
- 01 - AI data strategy - Data procurement and storage
- 02 - What you should know
1. AI Data Strategy
- 03 - Strategic decision-making in AI product development
- 04 - Data strategy vocabulary
- 05 - ML-driven AI vs. generative AI - A strategic overview
- 06 - The role of data strategy in AI product success
- 07 - Aligning data with business goals for AI product development
2. Data Procurement and Sources
- 08 - Sourcing structured data for ML-driven AI products
- 09 - Best practices for sourcing unstructured data
- 10 - Understanding bias in traditional ML systems
- 11 - Bias in generative AI - Challenges and mitigation strategies
- 12 - Framework for bias mitigation in AI
- 13 - Building intelligent systems with data protection
- 14 - Open data platforms - Democratizing AI development
- 15 - Leveraging APIs for AI
- 16 - Building sustainable data ecosystems
3. Storage Considerations for AI Products
- 17 - Choosing scalable storage solutions for ML-driven AI
- 18 - Optimizing storage for performance in ML-driven AI
- 19 - Data security and compliance in AI product development - On-premise, local AI
- 20 - Future-proofing data storage for AI products
4. Case Study Assessments
- 21 - Design a real-world data strategy for an AI-powered product
Conclusion
- 22 - Next steps