Advanced LLMOps: Deploying and Managing LLMs in Production
1h 45mAdvanced2024-07-19
Authors
Soham Chatterjee
Machine Learning Lead
Archana Vaidheeswaran
Course details
Cutting-edge artificial intelligence technologies are changing the world. But without proper deployment and management, your applications may never reach their full potential. Worse, they could simply fail or even cause critical errors in your systems. As more organizations are incorporating large language models into their workflows, there's an increasing need for professionals skilled in deploying and monitoring these models effectively, responsibly, and securely in production environments. In this course, learn the advanced techniques and best practices for deploying and monitoring LLMs in production environments. Explore LLM deployment options, handling API limitations, performance monitoring techniques, prompt management, addressing hallucinations, and more. Plus, learn about security and cost considerations, and test your learning with challenges and solutions.
Skills covered
Software DeploymentNatural Language Processing (NLP)AdvancedGenerative AIArtificial Intelligence (AI)Network and System Administration
Concepts
0. Introduction
- 01 - Deploying LLMs for production
- 02 - Working in Google Colab
1. Deployment Options for LLMs
- 03 - Overview of deployment options
- 04 - Deploying via APIs
- 05 - Using fine-tuned models for deployment
- 06 - Custom models - Building and deployment
2. Handling API Limitations
- 07 - Understanding API limitations
- 08 - Strategies to handle endpoint uptime limitations
- 09 - Mitigating latency issues in LLM deployment
- 10 - Challenge - API limitations for LLM deployment
- 11 - Solution - API limitations for LLM deployment
3. Deployment Architecture
- 12 - Vector databases for LLM deployment
- 13 - Agents in LLM deployment
- 14 - Chains in LLM deployment
- 15 - Challenge - Deploy a simple RAG application using an API
- 16 - Solution - Deploying a simple RAG application using an API
4. Monitoring LLM Performance
- 17 - Introduction to LLM performance monitoring
- 18 - Addressing hallucinations in LLMs
5. Advanced Deployment Techniques
- 19 - Prompt management for LLM deployment
- 20 - Evaluating LLMs in production
- 21 - Challenge - Evaluating LLM systems
- 22 - Solution - Evaluating LLM systems
6. Security and Cost Considerations
- 23 - Security considerations for LLMs in production
- 24 - Balancing costs and performance in LLM deployment
- 25 - Strategies for cost-effective LLM deployment
- 26 - Challenge - Estimating costs of an LLM API
- 27 - Solution - Estimating costs of an LLM API
Conclusion
- 28 - Next steps