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icon icon Building AI Intuition

Connecting the dots...

icon icon Building AI Intuition

Connecting the dots...

  • Home
  • ML Basics
  • Model Intuition
  • Encryption
  • Privacy Tech
  • Concepts
  • Musings
  • About
  • Home
  • ML Basics
  • Model Intuition
  • Encryption
  • Privacy Tech
  • Concepts
  • Musings
  • About
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Home/About

About

I am a product leader with over 20 years of experience driving product strategy, developing 0-to-1 products and scaling them to billions of users at companies like Microsoft, Meta, and Walmart.

My career lives at the intersection of Digital Ads, AI, and Privacy Infrastructure. With an MBA and a Master’s in Computer Science (AI/ML), I translate complex business problems into engineering challenges, moving seamlessly between the languages of data science, systems architecture, and executive strategy.

I’m a firm believer in platform-level thinking. In the AI era, I believe the best PMs don’t just write requirements—they prototype and build alongside their technical teams.

However, AI can be daunting. The math is heavy and the architectures are opaque. I created MLfunda to strip away the academic fluff and provide the “technical intuition” behind AI/ML and privacy tech. My goal is to show you how these systems actually work, minus the jargon.

Related Posts:

  • [C1] What Machines Actually Do (And What They Don't)
  • [ML 1] AI Paradigm Shift: From Rules to Patterns
  • [PET 1] Privacy Enhancing Technologies - Introduction
  • [ML x] Machine Decision: From One Tree to a Forest
  • [PET 1.c] Privacy Enhancing Technologies (PETs) — Part 3
  • [PET 1.b] Privacy Enhancing Technologies (PETs) — Part 2

Categories

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ML Basics

Back to the basics

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Model Intuition

Build model intuition

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Encryption

How encryption works

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Privacy Tech

What protects privacy

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Musings

Writing is thinking

Recent Posts

  • Exploring “Linear” in Linear Regression
  • The curious case of R-Squared: Keep Guessing
  • [C1] What Machines Actually Do (And What They Don’t)
  • [ML x] Machine Decision: From One Tree to a Forest
  • [MI 3] Seq2Seq Models: Basics behind LLMs
  • [MU 1] Advertising in the Age of AI
  • [MI 1] An Intuitive Guide to CNNs and RNNs
  • How Smart Vector Search Works
  • [PET 1.c] Privacy Enhancing Technologies (PETs) — Part 3
  • [PET 1.b] Privacy Enhancing Technologies (PETs) — Part 2
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