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README.md

Chapter 8: Model Deployment Optimizations

Questions and Answers

Q: How does pruning enhance model efficiency?

A: Pruning reduces the size of a model by removing less important neurons, which can lead to faster inference times and reduced memory usage without significantly impacting the model's performance.

Q: What is post-training quantization with GPTQ?

A: Post-training quantization with GPTQ (Generalized Poisson Training Quantization) involves reducing the precision of the model's parameters after training, which can lead to reduced model size and faster execution without major loss in accuracy.

Q: How do A/B testing and shadow deployment differ in deployment strategies?

A: A/B testing involves directing a portion of traffic to a new model to compare its performance against the existing model. In contrast, shadow deployment runs a new model in parallel with the existing one without directing real user traffic to it, primarily for testing and evaluation purposes.

Q: How do model deployment optimizations impact overall performance and scalability?

A: Optimizations in model deployment, such as model compression, efficient hardware utilization, and load balancing, can significantly improve performance, reduce costs, and ensure scalability to handle varying loads.

Chapters

  • Chapter 1 - Generative AI Use Cases, Fundamentals, Project Lifecycle
  • Chapter 2 - Prompt Engineering and In-Context Learning
  • Chapter 3 - Large-Language Foundation Models
  • Chapter 4 - Quantization and Distributed Computing
  • Chapter 5 - Fine-Tuning and Evaluation
  • Chapter 6 - Parameter-efficient Fine Tuning (PEFT)
  • Chapter 7 - Fine-tuning using Reinforcement Learning with RLHF
  • Chapter 8 - Optimize and Deploy Generative AI Applications
  • Chapter 9 - Retrieval Augmented Generation (RAG) and Agents
  • Chapter 10 - Multimodal Foundation Models
  • Chapter 11 - Controlled Generation and Fine-Tuning with Stable Diffusion
  • Chapter 12 - Amazon Bedrock Managed Service for Generative AI

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