AI Glossary · Data & Platform
Fine-tuning
Continuing to train a pre-trained model on domain- or task-specific data to specialize its behavior, tone or knowledge.
Definition
What is Fine-tuning?
Fine-tuning is continuing to train a pre-trained model on domain- or task-specific data to specialize its behavior, tone or knowledge.
- Category
- Data & Platform
- Glossary set
- 14 related terms
- Audience
- Enterprise AI leaders
Why does Fine-tuning matter in enterprise AI?
Fine-tuning matters in enterprise AI platforms because it affects data readiness, governance, scalability, and the operating foundation for AI use cases.
Related terms in Data & Platform
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- Bring Your Own Model (BYOM)
- A platform pattern that lets enterprises deploy fine-tuned or proprietary models inside a vendor's runtime instead of using only the vendor's default model.
- Data Fabric
- An architecture that unifies distributed data via metadata, semantic models and active governance — the layer AI depends on to reason across silos.
- Data Lakehouse
- A unified store combining lake economics with warehouse governance (Databricks, Snowflake, Iceberg). The default AI training and analytics substrate.
- Embedding
- A numeric vector representing meaning of text, image or audio, enabling semantic search, clustering and retrieval-augmented generation.
- Feature Store
- A managed catalog of reusable ML features with online and offline serving — key to consistent training and inference.