Machine learning guide: How to leverage ML for research

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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.

Vendor:
Domino
Posted:
Aug 29, 2024
Published:
Aug 29, 2024
Format:
PDF
Type:
EBRIEF
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