Very thought provoking. The balance between responsible regulatory postures and innovation support will be critical, and I hope that governments are well advised in navigating this. Arpad's comments about smaller niche players were interesting, as I can see the potential for solving narrower business problem sets with a more focused approach and data. That said, we have seen so many software vendors trying to become everything to everyone. I enjoyed this and will watch for further episodes--Thanks!
In your opinion what do you envision the industry role(s) of engineers who specialize in machine learning will be as the architecture of ML models becomes more standardized and more accessible? I am concerned that my economic prospects as an electrical engineer who specializes in machine learning will be made obsolete very quickly in the AI field due to AI code generation models that can write effective and deployable standardized ML architecture with high efficiency.
Good news: As we scale LLMs to enterprise-grade, ML expertise remains not just relevant but critical. The journey to enterprise readiness goes beyond basic model deployment, involving deep optimization across the entire toolchain-from nuanced entity recognition and efficient data extraction and chunking to tuning embedding and indexing, strategic retrieval optimization, advanced prompt engineering, and fine-tuning models using techniques like LoRA. The role of an ML/AI architect evolves into that of a master tuner, harmonizing each element to enhance overall performance significantly. The challenge ahead is to dive deep into the mechanics of managing and elevating the performance of the end-to-end processing chain. Embrace emerging tools like llamaIndex, PromptFlow, and LangChain, and master tuning methodologies that unlock new levels of effectiveness, improving RAG performance. This journey is not just about maintaining relevance; it's about leading the charge in the transformative use of AI in business, driving innovations that redefine what's possible. Thank you for the question - we will focus the next episode on AI skills and learning and what it takes to achieve AI leadership.
Very thought provoking. The balance between responsible regulatory postures and innovation support will be critical, and I hope that governments are well advised in navigating this. Arpad's comments about smaller niche players were interesting, as I can see the potential for solving narrower business problem sets with a more focused approach and data. That said, we have seen so many software vendors trying to become everything to everyone. I enjoyed this and will watch for further episodes--Thanks!
Thanks for the comments. Good point. Focus is a hard think for many software companies.
In your opinion what do you envision the industry role(s) of engineers who specialize in machine learning will be as the architecture of ML models becomes more standardized and more accessible? I am concerned that my economic prospects as an electrical engineer who specializes in machine learning will be made obsolete very quickly in the AI field due to AI code generation models that can write effective and deployable standardized ML architecture with high efficiency.
Good news: As we scale LLMs to enterprise-grade, ML expertise remains not just relevant but critical. The journey to enterprise readiness goes beyond basic model deployment, involving deep optimization across the entire toolchain-from nuanced entity recognition and efficient data extraction and chunking to tuning embedding and indexing, strategic retrieval optimization, advanced prompt engineering, and fine-tuning models using techniques like LoRA. The role of an ML/AI architect evolves into that of a master tuner, harmonizing each element to enhance overall performance significantly.
The challenge ahead is to dive deep into the mechanics of managing and elevating the performance of the end-to-end processing chain. Embrace emerging tools like llamaIndex, PromptFlow, and LangChain, and master tuning methodologies that unlock new levels of effectiveness, improving RAG performance. This journey is not just about maintaining relevance; it's about leading the charge in the transformative use of AI in business, driving innovations that redefine what's possible.
Thank you for the question - we will focus the next episode on AI skills and learning and what it takes to achieve AI leadership.
sounds like the plotline of the movie Wargames lol