With Foundation Model Training, Azure Databricks users can use their own data to customize a foundation model to optimize performance for their specific application.
By fine-tuning or continuing training of a foundation model, organizations can train their own model using significantly less data, time, and compute resources versus training a model from scratch.
Azure Databricks users have everything in a single platform: their own data to use for training, the foundation model to train, checkpoints saved to MLflow, and the model registered in Unity Catalog ready to deploy.
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