Machine learning guide: How to leverage ML for research
As AI and ML advance, organizations must navigate data management, workflows, and model lifecycles for effective R&D. This EBRIEF discusses key IT aspects for ML research, including:
- Robust ML platforms for consistent research pipelines, featuring environment management, workflow orchestration, and model lifecycle management.
- Data lineage, auditability, and governance for traceability and reproducibility in data and analytics.
- Automated workflows and task orchestration for efficient bioinformatics.
Addressing these IT needs enables research teams to accelerate discoveries and improve outcomes. Discover more in the full EBRIEF.