LLM Optimization Part 4 - 5 Techniques to reduce cost of LLM implementation

#llm #generativeai #costreduction #improveperformance
While the initial hype surrounding LLMs is settling, enterprises are shifting their focus towards practical AI deployment with cost-effectiveness in mind.
Today, we'll delve into the world of optimizing enterprise AI budgets, specifically focusing on Large Language Models (LLMs). Generative AI and LLMs are becoming the key differentiators of modern business operations. While the initial hype surrounding LLMs is settling, enterprises are shifting their focus towards practical AI deployment with cost-effectiveness in mind.
This video will equip you with five key techniques to reduce LLM implementation costs:
1. Choosing the most suitable foundation model.
2. Fine-tuning for specific use cases.
3. Model compression for efficient deployment.
4. Inference optimization for faster processing.
5. Optimizing resource utilization for cost savings.
This video is part of the LLM Optimization Series
LLM Optimization Part 5 - 5 Techniques for Superior Output from LLM
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LLM Optimization Part 4 - 5 Techniques to reduce cost of LLM implementation
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LLM Optimization LLM Part 3 - Improve LLM accuracy with GraphDB
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LLM Optimization LLM Part 2 - Large Language Model to Small Language Model - Quantization simplified
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LLM Optimization Part 1 - Calculating the True Cost of LLM
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[@imanebella4131]

Hello friends, Can anyone recommend a solid, up-to-date training program(s) that cover different cloud infrastructure services specifically aimed at deploying solutions using LLM models of any kind? open source or close source? (Agnostic arbitrage approach) If you have any suggestions or experiences to share on the subject, I'd love to hear them! Ideal training courses should cover: The main cloud services (IaaS, PaaS, SaaS) Deploying and hosting open source LLMs Data and storage management for LLMs Optimizing performance and scalability Security and governance of LLM solutions.