Netflix
A/B Testing & Experimentation
Netflix demonstrates how experimentation turns product decisions into measurable business evidence. I analyzed how A/B testing, user behavior data, and performance metrics help evaluate design changes before they are launched at scale.
Spotify
Recommendation Systems
Spotify shows how recommendation systems create a personalized product experience at scale. I explored how collaborative filtering, user behavior signals, content data, and feedback loops work together to improve music discovery and user engagement.
Uber
Forecasting & Dynamic Pricing
Uber uses predictive analytics to support real-time operational decisions. I studied how demand forecasting and dynamic pricing help balance rider demand with driver supply, improve marketplace efficiency, and manage pricing, availability, and wait times.
Waymo
Deep Learning & AI Strategy
Waymo highlights the complexity of deploying AI in a high-stakes physical environment. I examined how deep learning, sensor fusion, computer vision, and world models support autonomous driving, while also showing why safety validation, edge cases, and public trust are critical for adoption.
Airbnb
Marketplace AI & Pricing
Airbnb shows how AI can shape the performance of a two-sided digital marketplace. I looked at how search ranking, pricing signals, trust systems, and demand patterns help coordinate guests, hosts, and market conditions at scale.
Epic Healthcare
Agentic AI & Insurance Advocacy
Epic Healthcare connected the course concepts to agentic AI and workflow automation. I built n8n agents that used tools to retrieve policy and chart evidence, generate draft outputs, and demonstrate why human oversight, auditability, and AI governance are essential in high-stakes workflows.
Key Takeaways
What I learned across the labs
AI depends on data quality.
Even advanced systems can give weak results if the data, tools, or business context are incomplete.
Workflow fit matters.
The value of AI comes from how well it supports a real decision or process, not just from the model itself.
Human oversight still matters.
High-stakes AI systems need review, audit trails, source evidence, and clear escalation rules.