Production Inference

vLLM, high-throughput serving

Author: AI Learning Club Team · Difficulty: Advanced · Duration: 1 hr 55 min · Modules: 22

Modules

1. Introduction to Production Inference (57 min)
2. Overview of vLLM (57 min)
3. TensorRT Fundamentals (57 min)
4. Batching Strategies for Inference (57 min)
5. Load Balancing Techniques (57 min)
6. Cost Optimization in Model Serving (57 min)
7. High-Throughput Serving Architectures (57 min)
8. Model Quantization for Efficiency (57 min)
9. Distributed Inference Systems (57 min)
10. Monitoring and Logging in Production (57 min)
11. Scaling Inference Workloads (57 min)
12. Security Considerations for Model Serving (57 min)
13. Case Studies in Production Inference (57 min)
14. Best Practices for Model Deployment (57 min)
15. Advanced Batching Techniques (57 min)
16. Dynamic Load Balancing (57 min)
17. Cost-Benefit Analysis for Inference (57 min)
18. High-Throughput Serving Case Studies (57 min)
19. Model Serving in Multi-Cloud Environments (57 min)
20. Future Trends in Production Inference (57 min)
21. Capstone Project: Deploying a Scalable Inference System (57 min)
22. Resources & References (2 min)

Frequently Asked Questions

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