LLMOps
Operate LLM applications in production — evals, prompt versioning, and observability.
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After this skill you can…
- Set up LangSmith or Langfuse for LLM tracing
- Version and test prompts in a CI pipeline
- Monitor token costs, latency, and quality metrics
Prerequisites
Watch (10 videos)
LLMOPS 06: CI/CD Deployment with AWS ECS & Fargate | End-to-End GenAI Project Deployment
→ Deploy GenAI models with AWS ECS and Fargate→ Implement CI/CD pipelines for LLMOPS
Deploy ComfyUI Docker Container on MonsterAPI
→ Deploy custom LLM models on MonsterAPI→ Finetune LLMs with GPU computing
Detailed LLMOPs Project Lifecycle
→ Deploy LLMOPs projects→ Take AI projects to production
4. LLM Ops Infrastructure: Model Serving, RAG Pipelines, and Observability
→ Build LLM Ops Infrastructure→ Implement model serving and RAG pipelines
Model CI/CD Course: LLM Evaluation results
→ Deploy LLM models to production→ Automate LLM evaluation pipelines
OpenClaw is open! Run your 24x7 Clawdbot on a Secure VPS!
→ Deploy an AI assistant on VPS→ Use Docker for AI deployment
Build and Deploy a GenAI App with RAG on AWS Cloud | Step-by-Step Tutorial
→ Deploy GenAI app on cloud→ Build RAG-based chatbot
Develop Build and Deploy LLM Apps using GitHub Models and Azure AI Foundry | BRK107
→ Deploy an LLM model to production using Azure AI Foundry→ Build an LLM app using GitHub Models
Workshop: MLOps: End-to-End Hugging Face Transformers with the Hub & SageMaker Pipelines
→ Deploy Hugging Face Transformers to production→ Create automated MLOps pipelines
Scaling your tuned models with Cloud Run
→ Deploy AI models on cloud infrastructure→ Scale models for high performance
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