Podcast
Become an ML Product Manager: MLOps Platforms, Observability & Adoption
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Become an ML Product Manager: MLOps Platforms, Observability & Adoption
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Episode Overview
How do you become an ML product manager and build MLOps platforms that teams actually use? In this episode, Geo Jolly, a Technical PM at Glovo with a background from web/dev to data science and product management, walks through the practical skills and decisions that define the role.
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Chapter Summary
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- 0:00 - Episode Introduction: Product Management for Machine Learning
- 1:13 - Guest Overview: Geo and episode focus on AI Product Manager role
- 1:56 - Career Journey: From web/dev to data science to product management
- 6:28 - Glovo Role: Leading ML platform strategy and team responsibilities
- 8:41 - In-house MLOps Platform Strategy & Vendor Evaluation
- 9:50 - Product Manager Responsibilities: Roadmap, specs, and stakeholder balance
- 11:24 - Internal Platform Users as Customers: ROI and adoption considerations
- 13:44 - Platform Usability Costs: Productivity losses from poor tooling UX
- 15:19 - Backlog Prioritization: Grooming with engineering and Agile practices
- 16:44 - Outcome-Driven Problem Definition: Metrics over immediate solutions
- 18:25 - ML Observability: KPIs and measuring platform impact
- 19:29 - Avoiding Solution Bias: Techniques to resist jumping into solutions
- 21:06 - Collaborative Problem Breakdown: Workshops, interviews, and user input
- 22:15 - Core PM Skills: Communication, prioritization, and continuous learning
- 23:28 - Technical Literacy: Model architectures, data infra, and cloud concepts
- 25:31 - Technical Background Value: Why platform PMs need tooling familiarity
- 28:37 - Role Differences: Data Science Lead / Staff vs Technical ML Product Manager
- 31:28 - Release Governance & Rollout Strategy: Approvals, compliance, and timing
- 35:18 - Adoption Strategy: “Time to stakeholders” and internal rollouts
- 37:48 - Engineering Roles in ML Platforms: Backend, syseng, CI/CD, and K8s
- 40:14 - Embedded Data Scientists: Power users, developer advocates, and demos
- 42:14 - Agile for Data Science: Kanban, Scrum, and adapting to research work
- 44:56 - Transition Path: Moving from Data Scientist to Technical Product Manager
- 49:43 - Recommended Resources: Books and communities for PM transition
- 52:45 - Non-Technical Transitions: Feasibility of moving into ML product roles
- 55:44 - User Research for Internal Platforms: Surveys and Happiness Reports
- 57:20 - ML Quality Assurance: Model validation, shadowing, and release checklists
- 59:52 - Scrum Master to PM Advice: Leverage Agile skills and learn ML basics
- 1:01:51 - Final Thoughts: PM demands, scope, and career realities