CTO of Etiq AI, Raluca Crisan, joins us to explore what it really takes to build responsible, scalable AI in today’s high-pressure, fast-iterating tech environments. Drawing from her experience in data science and model governance across startups and impact-driven organizations like Zinc, Raluca shares how teams can move from reactive fixes to proactive safeguards without slowing innovation down.

Unpack why most data scientists still struggle with bias detection and testing, how orchestration tooling is evolving to support real-world deployment cycles, and what it means to operationalize responsibility from inside the data pipeline. The conversation touches on invisible risks in behavioral data, lessons from building testing tools that data scientists actually want to use, and the nuanced challenge of debugging AI failures in live environments.

We also look at why generative AI has accelerated urgency around model oversight, how LLMs mirror user bias, and why automation-first approaches to testing may be key to unlocking trust at scale.

Tune in for a wide-ranging discussion on responsible AI, emergent failure modes, and what it takes to make testing as intuitive, and indispensable, as model training.

MLOps London Meetup: https://www.meetup.com/mlopslondon/

Learn more about Etiq AI: https://www.etiq.ai/

In this episode of MLOps London On Air, host Alex Housley is joined by Saswati Saha Mitra, former Director of Insights at WhatsApp, to dive into what it really takes to build impactful AI-first products. Drawing from her leadership experience at WhatsApp, Uber, and Google, Saswati shares how research, data science, and engineering teams can meaningfully collaborate, even in highly constrained environments like end-to-end encrypted platforms.

They unpack lessons from scaling insights during the COVID misinformation crisis on WhatsApp, how to build with privacy-by-design while supporting business needs, and why collaboration models are evolving in leaner, more async organizations. The conversation also explores emerging trends like vibe coding, agentic systems, LLM interpretability, and how AI is transforming user research, dev tooling, and product strategy.

Tune in for a wide-ranging discussion on the future of AI collaboration, responsible scaling, and what it means to lead with insight in a rapidly shifting tech landscape.

Linkedin: https://www.linkedin.com/in/saswatisaha-mitra/

In Episode 2 of MLOps London On Air, Alex Housley sits down with AI veteran and innovation strategist Evangelos Simoudis. With over 40 years of experience from rule-based systems to today's generative models, Evangelos offers a masterclass in how businesses can move beyond pilots and hype to build truly transformational AI programs.

Tune in for insights on strategic foresight, organizational readiness, agentic AI, and why the future of MLOps requires both boardroom vision and developer execution.

Host: Alex Housley

Guest: Evangelos Simoudis

In this premiere episode of MLOps London On Air, host Alex Housley, Founder of Seldon, sits down with Prerna Kaul, Group Product Manager for GenAI at Panasonic Well, to explore the high-stakes world of AI deployment at scale and the ethical, technical, and strategic decisions shaping its future.

With over 14 years of experience in AI/ML at Amazon, Moderna, and now Panasonic, Prerna shares her journey from engineering to product leadership, and how she’s helped scale GenAI platforms in enterprise environments. She dives into strategies for building responsible AI, explains why she chose Anthropic’s Claude and the role of Constitutional AI, and unpacks the real-world challenges of compute, governance, and data. Prerna also offers practical advice on aligning AI use cases with business strategy—and shares bold predictions for the future of AI agents, enterprise adoption, and regulation. Whether you're a startup going from zero to one or scaling AI in a large enterprise, this episode is packed with valuable insights, war stories, and practical frameworks from someone who's been at the forefront of GenAI transformation.

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