How does it integrate with SpecWeave increments?

Models are automatically registered during the increment completion workflow, mapping specific code changes and experiments directly to the resulting model versions for complete traceability.

How does this skill help with model reproducibility?

It tracks every model artifact alongside its specific training metadata, feature schema, and dataset version, ensuring any past model can be fully audited or recreated.

Can I use external registries like MLflow?

Yes, the skill includes built-in providers for MLflow as well as custom storage backends like S3, GCS, or Azure Blob Storage.

What happens if a production model starts underperforming?

You can use the rollback command to manually revert to a previous stable version, or set up auto-rollback triggers that activate if error rates or latency exceed your thresholds.

ML Model Registry

Name: ML Model Registry
Author: anton-abyzov

byanton-abyzov

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데이터 과학 및 ML

Manages the complete machine learning model lifecycle through centralized versioning, staging pipelines, and automated metadata tracking within the SpecWeave framework.

소개

The ML Model Registry skill provides a standardized system for tracking and managing machine learning models from development through production. It addresses critical MLOps challenges such as model reproducibility, fragmented metadata, and risky deployments by offering a single source of truth for version history and model lineage. By integrating directly with SpecWeave increments, it enables developers to seamlessly promote models between staging environments, perform safe rollbacks based on automated performance triggers, and maintain comprehensive audit trails for regulatory compliance.

주요 기능

Automated staging pipeline management from development to production
Comprehensive model comparison tools for performance and latency metrics
One-command rollbacks with configurable performance and error triggers
Centralized versioning with semantic versioning (Major/Minor/Patch) support
13 GitHub stars
Detailed metadata tracking including hyperparameters, metrics, and lineage

사용 사례

Promoting a validated model from staging to production with a clear audit trail
Implementing automated safety triggers to rollback models that exceed latency thresholds
Investigating model lineage to identify the exact dataset and code version used for training

소개

주요 기능

Automated staging pipeline management from development to production
Comprehensive model comparison tools for performance and latency metrics
One-command rollbacks with configurable performance and error triggers
Centralized versioning with semantic versioning (Major/Minor/Patch) support
13 GitHub stars
Detailed metadata tracking including hyperparameters, metrics, and lineage

사용 사례

Promoting a validated model from staging to production with a clear audit trail
Implementing automated safety triggers to rollback models that exceed latency thresholds
Investigating model lineage to identify the exact dataset and code version used for training