ML Regression Prevention via Safe Fine-Tuning Cycles
Part of the Guard8.ai Ecosystem
TeacherGuard prevents ML regression by automating safe fine-tuning cycles. It acts as a pre-deployment validation gate that ensures new edge case fixes never break existing functionality.
- Edge case fix → immediate deployment → breaks 20 baseline tests
- 4-8 hours manual debugging per incident
- 30% regression rate in production
- Customer trust erosion
- Batch Processing: Collects edge cases into batches (default: 15 cases)
- Iterative Training: Runs until 100% pass on baseline + new cases
- Safe Deployment: Auto-deploys only when both test sets fully pass
- Audit Trail: Full logging for compliance and debugging
from teacher_guard import TeacherGuard, TestCase
# Define your baseline tests (contract that must always pass)
baseline = [
TestCase(id="base-1", input_data="hello", expected_output="greeting"),
TestCase(id="base-2", input_data="bye", expected_output="farewell"),
]
# Initialize TeacherGuard
guard = TeacherGuard(
baseline_tests=baseline,
model=your_model,
fine_tune_fn=your_fine_tune_function,
validate_fn=your_validate_function,
deploy_fn=your_deploy_function, # optional
alert_fn=your_alert_function, # optional
)
# Register edge cases as they're discovered
guard.register_edge_case(
TestCase(id="edge-1", input_data="hola", expected_output="greeting")
)
# Training auto-triggers when batch is full
# Or manually trigger:
guard.train()| Parameter | Default | Description |
|---|---|---|
batch_size |
15 | Edge cases needed before training |
max_iterations |
10 | Max training attempts |
pass_threshold |
100.0 | Required pass rate (%) |
Input: Guard8.ai edge case detection Process: Fine-tuning pipeline + validation Output: Safe deployments + audit logs Alerts: Configurable callback (Slack/email/etc.)
- Regression rate: < 5% (was 30%)
- Time to fix: < 2hrs (was 4-8hrs)
- Batch efficiency: ~15 cases per cycle
Proprietary - Guard8.ai