Automate machine learning model deployment, monitoring, and scaling with production-ready MLOps pipelines.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mlops
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install mlops using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is MLOps?
This skill provides a comprehensive framework for MLOps, bridging the gap between machine learning development and production engineering. It focuses on creating robust CI/CD pipelines, efficient model serving, and proactive monitoring to ensure models perform reliably in real-world environments. By implementing this Openclaw Skills resource, developers can transition from experimental notebooks to scalable, versioned, and reproducible AI systems.
The framework emphasizes critical engineering practices like avoiding training-serving skew, managing expensive GPU resources, and establishing clear versioning for models, data, and code. It is designed to help teams maintain high model availability while minimizing silent bugs and resource waste.
MLOps Use Cases
- Deploying machine learning models to production using automated CI/CD and DAG pipelines.
- Implementing drift detection and automated alerting systems for live models.
- Managing GPU infrastructure to optimize compute costs and prevent out-of-memory errors.
- Ensuring full reproducibility of models by versioning preprocessing steps and training data.
- Scaling inference services to handle varying loads without significant cold-start delays.
How MLOps Works
- Organize development workflows into modular CI/CD pipelines using DAGs to separate training and inference dependencies.
- Configure model serving infrastructure to handle large artifact loading and minimize cold-start latency.
- Establish monitoring protocols that track both technical infrastructure metrics and model-specific concept drift.
- Apply a three-tier versioning strategy covering the raw data, preprocessing logic, and the final model code.
- Optimize GPU allocation using specific request patterns to ensure resources are utilized efficiently without killing pods.
MLOps Setup
To integrate this skill into your workflow, initialize the configuration within your environment:
# Add the MLOps skill to your local agent
claw add mlops
# Review the provided documentation modules
ls .claw/skills/mlops/
Ensure you have access to your preferred model registry (like MLflow or DVC) and monitoring stack.
MLOps Data Schema & Taxonomy
The skill organizes MLOps knowledge and configurations into the following specialized files:
| File | Topic | Core Focus |
|---|---|---|
pipelines.md |
CI/CD and DAGs | Automation and dependency management |
serving.md |
Model serving | Inference performance and cold-start mitigation |
monitoring.md |
Drift and alerts | Technical and quality metric tracking |
reproducibility.md |
Versioning | Preprocessing and artifact consistency |
gpu.md |
Infrastructure | GPU requests and memory management |
MLOps Advanced Features
- Advanced Training-Serving Skew Prevention: Strategies to unify preprocessing across notebooks and production services.
- Smart GPU Sharing: Patterns for utilizing MIG and MPS for efficient hardware utilization.
- Cost-Benefit Drift Analysis: Logic to trigger retraining only when performance degradation outweighs the compute cost.
- Multi-Layer Versioning: Synchronized tracking of data, code, and model artifacts for audit-ready reproducibility.
- Delayed Ground Truth Handling: Specialized patterns for detecting concept drift when real-world labels lag behind predictions.
SKILL.md
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