Generative AI may feel like the new kid on the block, but it's not as different from traditional machine learning as you might think.

In this episode of Talking AI, we’re joined by Simba Khadder, Co-Founder & CEO of Featureform, and Omar Shanti, CTO of Hatchworks AI, to discuss how generative AI builds upon established ML principles and infrastructure. We explore the misconception that it exists as its own little island, when in reality, we see a clear continuity between classic ML and newer AI technologies.

Simba and Omar break down the unique aspects of GenAI, such as prompting and large language models (LLMs), while also highlighting the shared foundations with traditional ML. They also touch on the evolution of retrieval augmented generation (RAG), the ML vs. GenAI lifecycle, and what MLOps teams should consider when it comes to driving value in a business.

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Key moments:

The similarities and differences between generative AI and traditional machine learning
How prompts differentiate LLMs from earlier transformer models
The challenges of implementing AI in production versus creating pilot projects
How RAG has evolved and what it could mean for the future
The importance of data as the core foundation for both traditional ML and generative AI
How feature engineering in ML relates to embeddings in LLMs
How an ML lifecycle differs from a GenAI one

Key links:

Barbara Sattler, "The Concept of Motion in Ancient Greek Thought: Foundations in Logic, Method, and Mathematics" (Cambridge UP, 2020)