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EP 17 | Duke ML for PMs: The Modeling Process
Building a model is about more than just data—it’s about a repeatable process. This episode walks through the lifecycle of a machine learning project, focusing on the strategic decisions a Product Manager must navigate to ensure a model is production-ready.
Key Topics:
The 5-Step Process: From problem definition and data collection to model evaluation.
Feature & Algorithm Selection: How PMs influence which data is used and which model "flavor" fits the business goal.
The Bias-Variance Tradeoff: Understanding model complexity so you can troubleshoot "underfitting" or "overfitting" with your engineering team.
Validation & Testing: Why we use separate sets to prove a model actually works before it hits the real world.
Cross-Validation: Ensuring your model’s performance isn't just a fluke of the data.
Note: This is an AI-generated study resource created via NotebookLM based on Duke University’s ML for Product Managers curriculum and personal study notes.
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By Jack LakkapragadaBuilding a model is about more than just data—it’s about a repeatable process. This episode walks through the lifecycle of a machine learning project, focusing on the strategic decisions a Product Manager must navigate to ensure a model is production-ready.
Key Topics:
The 5-Step Process: From problem definition and data collection to model evaluation.
Feature & Algorithm Selection: How PMs influence which data is used and which model "flavor" fits the business goal.
The Bias-Variance Tradeoff: Understanding model complexity so you can troubleshoot "underfitting" or "overfitting" with your engineering team.
Validation & Testing: Why we use separate sets to prove a model actually works before it hits the real world.
Cross-Validation: Ensuring your model’s performance isn't just a fluke of the data.
Note: This is an AI-generated study resource created via NotebookLM based on Duke University’s ML for Product Managers curriculum and personal study notes.