Announcing Outliers: Learn from ML/AI Experts, Now in a Podcast Format

Over the years, we have talked with thousands of ML/AI researchers, founders, and practitioners, as well as with engineering leaders with deep infrastructure experience, all of whom have shared invaluable knowledge and practical guidance when it comes to building real-world ML/AI systems. Now, we’re releasing many of these impactful discussions in a podcast format, making it easier for you to learn from industry leaders whenever and wherever you want.

Why You Should Listen to Outliers

Each episode of Outliers features conversations with experts who have solved complex challenges in machine learning. Through their stories, you’ll gain practical insights that can be applied directly to your work — whether you're optimizing ML processes, building scalable AI systems, or strategizing on how to harness AI’s potential in your organization.

You can subscribe and listen to Outliers on your preferred platform:

• Apple Podcasts
• Spotify
• Outerbounds.com/outliers-podcast
• Or add it to your favorite podcast player using our RSS feed

A Preview of the First 10 Episodes

We’re launching with 10 episodes that tackle some of the most critical topics in AI and ML today. Here’s a glimpse of what you can expect:

Hilary Parker: How to Produce Sustainable Business Value with Machine Learning

In this episode, Hilary Parker—who has worked with companies like Stitch Fix, Etsy, and the Biden 2020 Campaign—shares insights into how machine learning can drive real, sustainable business value. You’ll learn about the common failure modes of ML-powered products and when it makes sense for companies to adopt ML. This episode is essential if you want to understand how ML can be a true value-driver for your organization.

Goku Mohandas: MadeWithML.com – Teaching Practical Machine Learning

In this conversation with Goku Mohandas, you’ll explore the journey from laptop data science to production ML. Goku shares practical advice on tools, workflows, and mental models needed to scale ML projects. You’ll also hear about lessons learned from large companies, making this episode particularly valuable if you’re working to move ML projects into production at scale.

Jacopo Tagliabue: Reasonable Scale Machine Learning – You’re Not Google and That’s Totally OK

Jacopo Tagliabue, Director of AI at Coveo, shares his expertise on how smaller companies can effectively implement machine learning without the resources of a tech giant. In this episode, you’ll learn what “reasonable scale” ML means and how you can build and operationalize ML systems that suit your organization’s unique needs. Jacopo was also our most recent podcast guest, expanding topics of this episode to the world of LLMs – stay tuned for the second part! 

Michelle Carney: Machine Learning and User Experience – Building ML Products for People

In this episode with Michelle Carney, you’ll discover why integrating user experience (UX) principles into machine learning products is crucial for success. Michelle explains how UX design can improve the impact of ML products and offers practical tips for ensuring that your ML solutions are intuitive and user-friendly.

Federico Bianchi: Large Language Models – Beyond Proofs of Concept

In this episode, Stanford researcher Federico Bianchi provides a deep dive into the current capabilities of LLMs. You’ll learn how to move beyond proof-of-concept LLMs and start extracting real business value while addressing risks such as privacy concerns and bias.

Stay Ahead with Real-Time Releases

Over the coming weeks, we’ll release many more episodes, giving you even more content as we catch up with our full library of discussions.

Subscribe Now

Subscribe to Outliers today and start learning from the experiences and lessons of the machine learning and data science community:

• Listen on Spotify
• Listen on Apple Podcasts
• Stream at Outerbounds.com/outliers-podcast
• Prefer a different podcast player? Use our RSS feed

Explore even more content on our YouTube channel, and join the community!