How Generative AI Is Impacting Data Engineering Teams

Jul 21, 2024

Lior Gavish, co-founder of Monte Carlo, discusses how data teams are evolving to support AI features and incorporating AI into their work. Topics include impact of generative AI, integrating AI models into data workflows, challenges in building with generative AI, and optimizing AI solutions for data teams.

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INSIGHT

Defining AI in the Current Context

While "AI" is often used synonymously with ChatGPT, this conversation focuses on generative AI.
Generative AI represents a foundational shift in AI usage, potentially leading to true machine intelligence.

INSIGHT

New Demands of Generative AI

Generative AI places new demands on data platforms, including prompt engineering and retrieval augmented generation (RAG).
The sheer size of these models also introduces new infrastructure requirements.

INSIGHT

Generative AI Technology Stack

Generative AI has introduced a new technology stack, including model APIs, RAG, fine-tuning tools, and orchestration frameworks.
Data pipelines remain crucial, marrying these models with existing organizational data.

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Summary
Generative AI has rapidly gained adoption for numerous use cases. To support those applications, organizational data platforms need to add new features and data teams have increased responsibility. In this episode Lior Gavish, co-founder of Monte Carlo, discusses the various ways that data teams are evolving to support AI powered features and how they are incorporating AI into their work.
Announcements

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Your host is Tobias Macey and today I'm interviewing Lior Gavish about the impact of AI on data engineers

Interview

Introduction
How did you get involved in the area of data management?
Can you start by clarifying what we are discussing when we say "AI"?
Previous generations of machine learning (e.g. deep learning, reinforcement learning, etc.) required new features in the data platform. What new demands is the current generation of AI introducing?
Generative AI also has the potential to be incorporated in the creation/execution of data pipelines. What are the risk/reward tradeoffs that you have seen in practice?
- What are the areas where LLMs have proven useful/effective in data engineering?
Vector embeddings have rapidly become a ubiquitous data format as a result of the growth in retrieval augmented generation (RAG) for AI applications. What are the end-to-end operational requirements to support this use case effectively?
- As with all data, the reliability and quality of the vectors will impact the viability of the AI application. What are the different failure modes/quality metrics/error conditions that they are subject to?
As much as vectors, vector databases, RAG, etc. seem exotic and new, it is all ultimately shades of the same work that we have been doing for years. What are the areas of overlap in the work required for running the current generation of AI, and what are the areas where it diverges?
- What new skills do data teams need to acquire to be effective in supporting AI applications?
What are the most interesting, innovative, or unexpected ways that you have seen AI impact data engineering teams?
What are the most interesting, unexpected, or challenging lessons that you have learned while working with the current generation of AI?
When is AI the wrong choice?
What are your predictions for the future impact of AI on data engineering teams?

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Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA