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Planning Machine Learning Products
- This article provides a structured approach to planning ML products, by writing a product design document.
- Start with a project kickoff. Encourage open collaboration and aim to surface the assumptions present in all cross-functional teams, ensuring alignment on product strategy and vision from day one.
- With your problem defined and why it matters, we can now document the requirements for delivering the project and set the scope.
- From a simple set of stories we can build actionable model requirements: What information is being sent to users? How will users be sent the warnings? What user-specific data can the system use?
- Thinking about users prompts us to embed ethics and privacy into our design while building products people trust.
- Once a basic dataset has been established you’ll need to understand the quality.
- Balancing business and technical metrics: Finding a “good enough” performance starts with understanding the distribution of events in the real world, and then relating this to how it impacts users (and hence the business).
- From the beginning include a plan to efficiently manage model lifecycle. The goal is to accelerate model iteration, automate deployment, and maintain robust monitoring for metrics and data drift.
- Once your prototype is ready, put it in the hands of people, no matter how embarrassed you are of it.
- As you move into development and deployment, you’ll inevitably find that requirements evolve and your experiments will throw up the unexpected. You’ll need to iterate!
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