Lilly Tech Systems
Master Data Management for ML
Master data is the foundation of consistent, reliable ML systems. Learn how to implement MDM frameworks that ensure your customer, product, and reference data is clean, unified, and ready for machine learning at enterprise scale.
Your Learning Path
Follow these lessons to master MDM for machine learning systems.
1. Introduction
What is master data, why it matters for ML, and the cost of poor master data management.
2. MDM Framework
MDM architecture patterns, implementation styles, and choosing the right approach.
3. Data Quality
Profiling, cleansing, standardization, and enrichment of master data for ML pipelines.
4. Entity Resolution
Matching, merging, and deduplicating records across systems using ML-powered techniques.
5. Integration
Integrating MDM with ML pipelines, feature stores, and real-time inference systems.
6. Best Practices
Organizational strategies, tool selection, and proven patterns for MDM success.
What You'll Learn
By the end of this course, you'll be able to:
Design MDM Systems
Choose and implement the right MDM architecture for your ML needs.
Ensure Data Quality
Profile, cleanse, and standardize master data for ML consumption.
Resolve Entities
Match and merge duplicate records across enterprise systems.
Integrate with ML
Connect MDM systems with feature stores and ML pipelines.