Stop Managing AI Projects Like Traditional Software

Lightning Lessons

Practical skills & tools to accelerate your career

Stop Managing AI Projects Like Traditional Software

Hosted by Bryan Bischof and Hamel Husain

940 students

What you'll learn

Learn Why Traditional Software Roadmaps Fail in AI Projects

We'll show you why conventional approaches to product development break down when building AI and what to do instead.

Prioritizing Work Through Effective Evaluation

Explore methods for creating evals that pinpoint where your AI is struggling, and how to prioritize improvements.

How to Adopt an Experimental Mindset

Learn to build AI products through iterative experiments rather than rigid roadmaps, with clear, measurable objectives.

Turn Failures Into Actionable Insights

Learn to break down complex AI capabilities into measurable stages that help you identify where to focus.

Why this topic matters

AI projects fail when managed like traditional software. Instead of fixed deadlines, successful AI teams focus on experiments. We'll share a proven framework that shifts your thinking from timelines to learning. This experimental mindset is what separates successful AI initiatives from struggling ones.

You'll learn from

Bryan Bischof

Head of AI at Theory Ventures

Bryan Bischof is currently Head of AI at Theory Ventures. He formerly led AI Engineering at Hex, where he spearheaded the development of Magic—the data science and analytics copilot. Bryan has worked all over the data stack leading teams in analytics, machine learning engineering, data platform engineering, and AI engineering. He started the data team at Blue Bottle Coffee, led several projects at Stitch Fix, and built the data teams at Weights and Biases. Bryan previously co-authored the book Building Production Recommendation Systems with O’Reilly, and teaches Data Science and Analytics in the graduate school at Rutgers. His Ph.D. is in pure mathematics.

Hamel Husain

ML Engineer with 20 years of experience.

Hamel Husain is a ML Engineer with over 20 years of experience. He has worked with innovative companies such as Airbnb and GitHub, which included early LLM research used by OpenAI, for code understanding. He has also led and contributed to numerous popular open-source machine-learning tools. Hamel is currently an independent consultant helping companies operationalize Large Language Models (LLMs).