Bias Mitigation: Databases often reflect historical biases present in the real world. ML models trained on such data can perpetuate and even amplify these biases, leading to discriminatory outcomes. Developing robust methods for detecting and mitigating bias within database-driven ML pipelines is crucial.
Scalability and Performance for Diverse Workloads:
Computational Intensity: Training complex ML models, especially deep learning models, requires substantial computational resources. Databases need to be able to handle both traditional transactional/analytical queries and the bursty, resource-intensive demands of ML workloads.
Real-time Processing: Many ML applications require real-time data ingestion and processing for immediate predictions. Ensuring low-latency access and efficient data flow within database systems for these scenarios is challenging.
Data Governance, Security, and Privacy:
Compliance: Regulations like GDPR, HIPAA, and CCPA impose accurate cleaned numbers list from frist database strict requirements on processed, and used. Integrating ML with databases necessitates robust mechanisms for data access control, encryption, auditing, and the "right to be forgotten" (machine unlearning).
Data Lineage and Provenance: Understanding the origin, transformations, and usage of data throughout the ML pipeline is vital for debugging, auditing, and ensuring transparency, especially in regulated industries.