1. Data Acquisition Strategy and Source Evaluation
5 lessonsLearn to identify, evaluate, and prioritize data sources based on quality, coverage, cost, and legal constraints.
2. Cleaning Workflows and Quality Control
5 lessonsBuild automated pipelines to detect and remove noise, duplicates, malformed content, and low-value material.
3. Provenance Tracking and Metadata Management
4 lessonsImplement systems to track data lineage, source attribution, and transformation history across pipelines.
4. Privacy, Security, and Sensitive Data Handling
5 lessonsDetect and protect personally identifiable information, proprietary content, and regulated data in ML pipelines.
5. Evaluation Set Hygiene and Contamination Prevention
4 lessonsDesign processes to prevent test data leakage and ensure evaluation metrics reflect true model capability.
6. Governance Frameworks and Policy Design
5 lessonsEstablish organizational policies for data usage, retention, licensing, and ethical review.
7. Diagnosing and Debugging Data Quality Issues
5 lessonsLearn systematic approaches to identify data problems that cause model failures, bias, or performance degradation.
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