Skip to main content

Model Fine Tuning

Fine-tune pre-trained models on custom datasets including transfer learning, learning rate scheduling, early stopping, and model comparison.

Complexity: Complex | Duration: 30m+ | Category: Devops

Tags: ml fine-tuning transfer-learning deep-learning

Workflow Diagram​

Steps​

Step 1: Base model selection​

Agent: ml

engineer - Choose pre-trained model (BERT, ResNet, etc.)

Step 2: Data preparation​

Agent: data

scientist - Format data for fine-tuning

Step 3: Layer freezing​

Agent: ml

engineer - Freeze early layers, unfreeze later layers

Step 4: Learning rate schedule​

Agent: ml

engineer - Use learning rate warmup and decay

Step 5: Fine​

Agent: tuning

ml-engineer - Train with small LR, monitor validation loss

Step 6: Early stopping​

Agent: ml

engineer - Stop when validation loss plateaus

Step 7: Checkpoint saving​

Agent: ml

engineer - Save best model based on validation metric

Step 8: Comparison​

Agent: data

scientist - Compare fine-tuned vs. base model performance

Usage​

To execute this workflow:

/workflow devops/model-fine-tuning.workflow

See other workflows in this category for related automation patterns.