Advanced LLMOps: Deploying and Managing LLMs in Production

Learn advanced techniques and best practices for deploying and monitoring large language models in production environments.

Introduction

• Deploying LLMs for production
• Working in Google Colab

1. Deployment Options for LLMs

• Overview of deployment options
• Deploying via APIs
• Using fine-tuned models for deployment
• Custom models: Building and deployment

2. Handling API Limitations

• Understanding API limitations
• Strategies to handle endpoint uptime limitations
• Mitigating latency issues in LLM deployment
• Challenge: API limitations for LLM deployment
• Solution: API limitations for LLM deployment

3. Deployment Architecture

• Vector databases for LLM deployment
• Agents in LLM deployment
• Chains in LLM deployment
• Challenge: Deploy a simple RAG application using an API
• Solution: Deploying a simple RAG application using an API

4. Monitoring LLM Performance

• Introduction to LLM performance monitoring
• Addressing hallucinations in LLMs

5. Advanced Deployment Techniques

• Prompt management for LLM deployment
• Evaluating LLMs in production
• Challenge: Evaluating LLM systems
• Solution: Evaluating LLM systems

6. Security and Cost Considerations

• Security considerations for LLMs in production
• Balancing costs and performance in LLM deployment
• Strategies for cost-effective LLM deployment
• Challenge: Estimating costs of an LLM API
• Solution: Estimating costs of an LLM API

Conclusion

• Next steps