What Netflix and Swiggy Teach Us About Better ML Communication
ML fails more from miscommunication than bad modeling. Learn how companies succeed by translating models into meaningful conversations.
Table of Contents
1. Why ML Projects Fail to Deliver
2. The Netflix Analogy: Talking in ROC-AUC vs Real Buzz
3. Mood Indigo Fest: Asking the Right Questions
4. Swiggy Combos: Unguided vs Guided Planning
5. Drawing Flowcharts Like We Did in School
6. Spotify MVP: A Case of Listening Broadly, Executing Sharply
7. Subject Matter Experts: Who’s Your Champion?
8. Gantt Charts & Milestones: Meetings That Matter
9. The Drama Skit Analogy: Before You Go Live
10. Final Takeaways
1. Why ML Projects Fail to Deliver
Miscommunication is the silent killer of most ML projects. Too often, teams obsess over model performance while neglecting alignment with business goals. Clarity on why the project matters is usually missing. This leads to misaligned expectations, scope creep, and poor adoption even if the model performs well.
Key reasons projects fail:
No shared understanding of goals
Overengineering without business input
Ignoring user context
Takeaway: Success lies more in shared understanding than in superior architecture.
2. The Netflix Analogy: Talking in ROC-AUC vs Real Buzz
You might report a 0.91 ROC-AUC, but your stakeholder really wants to know:
"Which 5 shows should we promote next?"
Q: Why is this a problem?
Because we speak in metrics, while the business speaks in outcomes.
What to do instead:
Translate metrics into user value.
Replace ROC-AUC with tangible predictions: “These shows will likely trend among this audience.”
Align every performance metric with a business decision.
3. Mood Indigo Fest: Asking the Right Questions
Suppose you're tasked with building a website for Mood Indigo. You assume it needs only event schedules. But stakeholders also need sponsor visibility, ticketing, merchandise sales, and hotel booking support.
This is a classic mistake: jumping to solutions without clarifying the problem.
Ask early:
Why is this product being built?
What are the expectations across departments?
Who are the users and what are their pain points?
How will success be measured?
Only by exploring the full scope can you deliver something valuable.
Source: moodi.org
4. Swiggy Combos: Unguided vs Guided Planning
In an unguided meeting, chaos prevails, data scientists talk GPU time, designers focus on UI elements, and product managers chase deadlines. No one is aligned.
A guided planning session asks these four questions:
Why are we building this?
What will success look like?
How could this fail?
When will we deliver which part?
Result: Teams align around priorities. The Swiggy “combo recommendation” team, for example, succeeded not just because of their models, but because they structured conversations well.
5. Drawing Flowcharts Like We Did in School
Remember the flowcharts we drew for making noodles or brushing teeth in school?
Apply the same for ML pipelines.
Benefits:
Makes complex systems understandable
Helps cross-functional teams visualize dependencies
Identifies bottlenecks before they occur
Source:- Machine Learning Engineering in Action, author:- Ben Wilson
Tip: If your ML system isn’t simple enough to be drawn, it’s probably too complex to manage.
6. Spotify MVP: A Case of Listening Broadly, Executing Sharply
Spotify’s “Discover Weekly” started with one goal: recommend 30 songs per user every week. No frills. No new artist push. No personalization layers initially.
They succeeded because they:
Focused on one clear user value
Released early, learned fast
Iterated based on user feedback
Source:- Spotify
Lesson: Don’t chase perfection in V1. Ship something specific, useful, and testable.
7. Subject Matter Experts: Who’s Your Champion?
You can’t consult the CEO every time a scope decision arises. The real guide is your Subject Matter Expert (SME)—the person who knows the domain inside out.
Role of an SME:
Validates if the model solves a real problem
Bridges the gap between business and data
Helps prioritize features and refine scope
Treat SMEs as:
Trusted advisors
Co-owners of the solution
Decision filters when trade-offs arise
8. Gantt Charts & Milestones: Meetings That Matter
Gantt charts aren't just project management fluff—they’re clarity tools.
They help:
Map timeframes to outcomes
Schedule meaningful reviews
Catch misalignments early
Key difference from daily standups:
Milestone-based reviews are not about “what we did,” but “what we proved.”
Use milestones to:
Validate functionality
Demo real progress
Address delays with data
9. The Drama Skit Analogy: Before You Go Live
Deploying a model is like staging a play.
You don’t just hand over the script. You rehearse, test lighting, prepare backups and only then do you perform.
Similarly, your ML model needs:
User Acceptance Testing (UAT)
Launch-readiness checklists
Fallback mechanisms in production
Lesson: Don’t assume deployment is the end. It’s a final performance and you need rehearsals.
10. Final Takeaways
Here’s a distilled checklist to keep your ML projects on track:
Meet with intent: clarify before building
Define success in business terms
Communicate metrics as outcomes
Validate MVPs before scaling
Involve SMEs early and often
Use visuals to reduce misinterpretation
Treat planning as architecture, not just admin
Launch only when basic reliability is ensured
Good ML in production isn’t just about science or math. It’s about orchestration, empathy, and execution.
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In the next article we will dive deeper into communication and logistics in ML Project.
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