Podcast
Build & Scale Data Products for AI: Roadmaps, MLOps, Customer Research & Metrics
Open original DataTalks.Club episode
Build & Scale Data Products for AI: Roadmaps, MLOps, Customer Research & Metrics
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Episode Overview
How do you move from proofs-of-concept to scalable AI data products that deliver measurable business value? In this episode, Greg Coquillo, a Technology Manager at Amazon who builds AI roadmaps for Private Brands’ product safety and compliance, walks through practical approaches for building and scaling data products, MLOps, customer research, and metrics.
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Chapter Summary
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- 0:00 - Episode Introduction & Guest Overview
- 1:43 - Career Background & Transition to AI Products
- 6:41 - Role & Responsibilities of Data Product Managers (Internal vs External)
- 14:03 - Customer Journey & Domain Knowledge for Data Professionals
- 18:01 - Customer Research Techniques: Interviews & Documentation
- 20:28 - Structuring Interviews with Business Partners & the Five Whys
- 23:20 - Hypothesis Testing & Working Backwards from Business Problems
- 26:25 - Product Sense & Product Mindset Explained
- 28:53 - Contributing to Roadmaps: Technical Input & T-Shirt Sizing
- 31:45 - Working Backwards: Problem-First Feature Design
- 35:34 - Roadmap Types: Technical Roadmaps, MLOps & Scaling Strategies
- 39:01 - Identifying Unscalable Manual Processes & ML Ops Prioritization
- 41:44 - Three-Year Roadmap: Prioritization by Impact, Effort & Cost
- 47:18 - Practical Roadmap Template in Excel: Problems → Solutions → Metrics
- 51:11 - Success Metrics & SMART Goals for Internal Data Platforms
- 53:27 - Operational Metrics: Pipeline Failures, SLAs & Data Quality
- 55:32 - Operating Without a PM: Identify Customers & Validate Work
- 57:44 - Team Autonomy & Aligning Mental Models for Product Success
- 58:42 - Career Advice: Learn Product Skills on the Job & Follow-up Resources