This usually happens because the pipeline expects a fixed schema.

Many models rely on strict feature ordering or predefined schemas. When a new feature is added upstream, downstream components may misalign inputs without explicit errors.

Use schema validation at pipeline boundaries to enforce expectations. Feature stores or explicit column mappings help ensure only expected features reach the model.

If your system allows optional features, handle them explicitly rather than relying on implicit ordering.

Common mistakes include:

• Assuming backward compatibility in data pipelines

• Skipping schema checks for performance

• Letting multiple teams modify data contracts informally

The takeaway is to treat feature schemas as versioned contracts, not informal agreements