Advanced LLMOps: Deploying and Managing LLMs in Production

سرفصل های دوره

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.

01 - Introduction

01 - Deploying LLMs for production

02 - Working in Google Colab

02 - 1. Deployment Options for LLMs

01 - Overview of deployment options

02 - Deploying via APIs

03 - Using fine-tuned models for deployment

04 - Custom models Building and deployment

03 - 2. Handling API Limitations

01 - Understanding API limitations

02 - Strategies to handle endpoint uptime limitations

03 - Mitigating latency issues in LLM deployment

04 - Challenge API limitations for LLM deployment

05 - Solution API limitations for LLM deployment

04 - 3. Deployment Architecture

01 - Vector databases for LLM deployment

02 - Agents in LLM deployment

03 - Chains in LLM deployment

04 - Challenge Deploy a simple RAG application using an API

05 - Solution Deploying a simple RAG application using an API

05 - 4. Monitoring LLM Performance

01 - Introduction to LLM performance monitoring

02 - Addressing hallucinations in LLMs

06 - 5. Advanced Deployment Techniques

01 - Prompt management for LLM deployment

02 - Evaluating LLMs in production

03 - Challenge Evaluating LLM systems

04 - Solution Evaluating LLM systems

07 - 6. Security and Cost Considerations

01 - Security considerations for LLMs in production

02 - Balancing costs and performance in LLM deployment

03 - Strategies for cost-effective LLM deployment

04 - Challenge Estimating costs of an LLM API

05 - Solution Estimating costs of an LLM API

08 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)