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Artwork for Generative AI in the Real World

Generative AI in the Real World

O'Reilly
Generative AI
AI Agents
Machine Learning
Large Language Models
Data Privacy
Synthetic Data
Artificial Intelligence
Product Management
A2A Protocol
Multimodal Data
Llms
Language Models
Google Cloud
Model Context Protocol (MCP)
Observability
Retrieval Augmented Generation
Cybersecurity
Github
Fraud Detection
Data Sharing

In 2023, ChatGPT put AI on everyone’s agenda. Now, the challenge will be turning those agendas into reality. In Generative AI in the Real World, Ben Lorica interviews leaders who are building with AI. Learn from their experience to help put AI to work in your enterprise.

PublishesMonthlyEpisodes42Founded9 months ago
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Artwork for Generative AI in the Real World

Latest Episodes

As a pandas core contributor and early Parquet adopter who built AI data pipelines at streaming company Tubi TV, Chang She saw firsthand why the traditional data stack breaks down for AI workloads—and founded LanceDB to fix it. Chang joined Ben Loric... more

As the founder and CEO of LevelUp Labs, Aishwarya Naresh Reganti helps organizations “really grapple with AI,” and through her teaching, she guides individuals who are doing the same. Aishwarya joined Ben to share her experience as a forward-deployed... more

YouTube

Post-training gets your model to behave the way you want it to. As AMD VP of AI Sharon Zhou explains to Ben on this episode, the frontier labs are convinced, but the average developer is still figuring out how post-training works under the hood and w... more

Synthetic data has been around for a long time, decades even. But as KPMG’s Fabiana Clemente points out, “That doesn’t mean there aren’t a lot of misconceptions.” Fabiana sat down with Ben to clarify some of the current applications of synthetic data... more

SwirlAI founder Aurimas Griciūnas helps tech professionals transition into AI roles and works with organizations to create AI strategy and develop AI systems. Aurimas joins Ben to discuss the changes he’s seen over the past couple years with the rise... more

YouTube

As the founder, editor, and lead writer of Turing Post, Ksenia Se spends her days peering into the emerging future of artificial intelligence. She joined Ben to discuss the current state of adoption: what people are actually doing right now, the big ... more

MLOps is dead. Well, not really, but for many the job is evolving into LLMOps. In this episode, Abide AI founder and LLMOps author Abi Aryan joins Ben to discuss what LLMOps is and why it’s needed, particularly for agentic AI systems. Listen in to he... more

In this episode, Laurence Moroney, director of AI at Arm, joins Ben Lorica to chat about the state of deep learning frameworks—and why you may be better off thinking a step higher, on the solution level. Listen in for Laurence’s thoughts about posttr... more

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Recent Guests

Chang She
CEO and co-founder of LanceDB
LanceDB
Episode: Chang She on Data Infrastructure for AI
Aishwarya Naresh Reganti
Founder and CEO of LevelUp Labs
LevelUp Labs
Episode: Aishwarya Naresh Reganti on Making AI Work in Production
Sharon Zhou
Vice President of AI at AMD
Advanced Micro Devices (AMD)
Episode: Sharon Zhou on Post-Training
Fabiana Clemente
Senior Director and Distinguished Engineer
KPMG
Episode: Fabiana Clemente on Synthetic Data for AI and Agentic Systems
Aurimas Griciūnas
Expert in AI Engineering and AI systems from SwirlAI, previously at Neptune.AI
SwirlAI
Episode: Aurimas Griciūnas on AI Teams and Reliable AI Systems
Ksenia Se
Founder and editor at Turing Post
Turing Post
Episode: The Year in AI with Ksenia Se
Abi Aryan
Author of the O'Reilly book on LLMOps and founder of Abide AI
Abide AI
Episode: The LLMOps Shift with Abi Aryan
Drew Breunig
Strategist at the Overture Maps Foundation and author of the upcoming Context Engineering Handbook
Overture Maps Foundation
Episode: Context Engineering with Drew Breunig
Emmanuel Ameisen
Researcher at Tropic focusing on interpretability in AI
Tropic
Episode: Emmanuel Ameisen on LLM Interpretability

Host

Ben Lorica
Host and interviewer

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Talking Points

Recent interactions between the hosts and their guests.

Sharon Zhou on Post-Training
Q: How should an enterprise think about the trade-offs between in-context learning and fine-tuning for post-training?
In-context learning is easy and fast but may not be as durable; supervised fine-tuning and adapters can yield more robust, private, and low-latency improvements, with the best approach depending on the use case, available data, and required performance.
Sharon Zhou on Post-Training
Q: Is there a clear path for enterprises to adopt post-training, or do they need to build everything from scratch?
It's a mix; for many enterprises it's about not trying to do everything in-house due to infrastructure needs, while still learning the process and leveraging existing tools and environments to guide behavior adaptation through targeted post-training, adapters, and RL where appropriate.
Sharon Zhou on Post-Training
Q: Give us your one to four sentence definition of what post-training is even at a high level.
Post-training is a type of training of a language model that gets it to behave in the way you want, enabling it to chat, use tools and APIs, reason, and think step by step to produce more accurate and usable outputs.
Aishwarya Naresh Reganti on Making AI Work in Production
Q: How important is model portability and harness engineering in current enterprise AI projects?
While model performance remains important, enterprises are increasingly prioritizing the application layer and portability, ensuring systems can swap models and adapt harnesses without rebuilding from scratch, to manage cost and risk.
Aishwarya Naresh Reganti on Making AI Work in Production
Q: What do non-data and AI people consistently get wrong when moving from traditional software to AI applications?
They often neglect data distribution and the importance of a robust calibration process, assuming models alone will deliver; the focus should shift to understanding workflows, context, and how users actually interact with the product.

Audience Metrics

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Frequently Asked Questions About Generative AI in the Real World

What is Generative AI in the Real World about and what kind of topics does it cover?

The show centers on practical, real-world deployments of generative AI and how enterprises can translate AI research into usable systems. Conversations frequently cover topics like AI engineering, MLOps/LLMOps, observability, edge AI, agent-enabled workflows, and enterprise adoption challenges, with guests ranging from senior engineers and researchers to product leaders and industry analysts. A recurring strength is translating complex AI concepts into actionable insights for teams building and operating AI-powered solutions in large organizations. The format often highlights practical use cases (fraud detection, data privacy, attribution, discovery, and cost/performance tradeoffs) and emphasizes the human and organizational dimensions of d... more

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1. Super Data Science: ML & AI Podcast with Jon Krohn
2. The AI Daily Brief: Artificial Intelligence News and Analysis
3. Think Fast Talk Smart: Communication Techniques
4. Syntax - Tasty Web Development Treats

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Generative AI in the Real World launched 9 months ago and published 42 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Generative AI in the Real World?

Recent guests on Generative AI in the Real World include:

1. Chang She
2. Aishwarya Naresh Reganti
3. Sharon Zhou
4. Fabiana Clemente
5. Aurimas Griciūnas
6. Ksenia Se
7. Abi Aryan
8. Drew Breunig

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