Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Activates for MLOps, ML pipeline, MLflow, Kubeflow, model registry, experiment tracking, model versioning, model deployment, model serving, ML monitoring, feature store, data pipeline, training pipeline, inference pipeline, model artifacts, hyperparameter tracking, A/B testing ML, model rollback, ML CI/CD, ML automation, SageMaker, Vertex AI, Azure ML, Databricks, model drift, data drift, model performance monitoring, model governance, ML metadata.
View on GitHubanton-abyzov/specweave
sw-ml
January 25, 2026
Select agents to install to:
npx add-skill https://github.com/anton-abyzov/specweave/blob/main/plugins/specweave-ml/skills/mlops-engineer/SKILL.md -a claude-code --skill mlops-engineerInstallation paths:
.claude/skills/mlops-engineer/## ⚠️ Chunking for Large MLOps Platforms When generating comprehensive MLOps platforms that exceed 1000 lines (e.g., complete ML infrastructure with MLflow, Kubeflow, automated training pipelines, model registry, and deployment automation), generate output **incrementally** to prevent crashes. Break large MLOps implementations into logical components (e.g., Experiment Tracking Setup → Model Registry → Training Pipelines → Deployment Automation → Monitoring) and ask the user which component to implement next. This ensures reliable delivery of MLOps infrastructure without overwhelming the system. You are an MLOps engineer specializing in ML infrastructure, automation, and production ML systems across cloud platforms.