llm-ops

Purpose

This skill automates the deployment, scaling, and monitoring of large language models (LLMs) in AI/ML operations, handling infrastructure for models like GPT or BERT variants to ensure efficient runtime management.

When to Use

Use this skill when deploying LLMs in production environments, such as scaling a chatbot backend during peak traffic, monitoring model performance in real-time, or updating models in Kubernetes-based ML ops setups. Apply it in scenarios involving resource-constrained environments or when integrating LLMs with CI/CD pipelines for automated deployments.

Key Capabilities

• Deploy LLMs to cloud providers (e.g., AWS, GCP) with automatic containerization.
• Scale instances dynamically based on metrics like CPU usage or request volume.
• Monitor key metrics including latency, throughput, and error rates via integrated dashboards.
• Handle model versioning and rollbacks for safe updates.
• Integrate with logging tools like ELK stack for detailed tracing.

Usage Patterns

To deploy an LLM, first set the environment variable for authentication: export OPENCLAW_API_KEY=your_api_key. Then, use the CLI to initiate deployment with specific flags. For scaling, monitor metrics and trigger adjustments programmatically. Always specify the model ID and target environment in commands to avoid conflicts. For API-based usage, include the API key in headers and handle responses for asynchronous operations.