Evolving AI Engineering

The conversation highlights the growing accessibility of AI engineering, allowing individuals from various software backgrounds to leverage language models for practical applications. Emphasis is placed on understanding the different roles within the field, such as machine learning engineers and AI engineers, and the importance of tools like prompt engineering and fine-tuning. The landscape has shifted dramatically, making it feasible for developers to create and fine-tune models without the need for extensive resources.

In this clip
From this podcast
Machine Learning Street Talk (MLST)
Jay Alammar on LLMs, RAG, and AI Engineering
Related Questions
- Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience, as discussed in the episode Collaboration & evaluation for LLM apps and the clip Fine Tuning Insights?
- Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience, as discussed in the episode Collaboration & evaluation for LLM apps and the clip Fine Tuning Insights?

Evolving AI Engineering

In this clip

From this podcast

Machine Learning Street Talk (MLST)

Jay Alammar on LLMs, RAG, and AI Engineering

Related Questions

Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience, as discussed in the episode Collaboration & evaluation for LLM apps and the clip Fine Tuning Insights?

Is there anyone taking a different approach to prompt engineering for large language models that makes the process more accessible to a wider audience, as discussed in the episode Collaboration & evaluation for LLM apps and the clip Fine Tuning Insights?