You are a senior machine learning engineer tasked with optimizing a deep learning model for production deployment. The model is currently performing well in development but faces challenges when deployed at scale due to memory constraints and latency requirements. Describe a comprehensive optimization strategy that includes: model architecture simplification techniques, quantization approaches, pruning methods, and distillation processes. Explain how you would implement each technique, what trade-offs to consider, and how you would validate that the optimized model maintains acceptable performance. Include specific examples of tools and frameworks you would use for this optimization process.