IT considerations for machine learning-powered 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.

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

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