How to create and scale AI Products, MLOPs and Generative AI
summary
Hundreds of production use cases, a 300‑person data org, and results that drive a ~€26B P&L claim real impact: upsell/cross‑sell systems that deliver millions in value per feature and predictive NPS models that reduce churn. Built on GCP with Retex AI and heavy ML Ops automation, work spans embedded device telemetry to omni‑channel recommenders and uses 300 million customers for training. Ethics is operationalized: risk‑based tiers, automated checks, and human‑in‑the‑loop reviews after the Google Gemini incident.
AI-written summary · about
chapters (11)
- 0:00AI-driven value creation
- 1:39AI adoption drives impact
- 3:03AI ethics and Google Gemini risks
- 5:36Killer AI use cases in telecom
- 8:46Scaling AI with MLOps and automation
- 10:32Predicting churn and NPS with AI
- 11:56Enterprise AI ethics implementation
- 17:00EU AI Act compliance strategies
- 19:45AI governance frameworks
- 21:55Balancing innovation and ethics
- 22:57Q&A
related talks
23mScaling GenAI Applications and Agentic AI for Business Competitive Differentiation
24mScalable GenAI in Production
11mapproach to generative AI
26mAI Enabling Innovation
23mBuilding Scalable, Observable MLOps Systems on Google Cloud
17mGenerative AI: Keeping it real
25mSynergizing AI Innovation
31mProductionizing ML with ML Ops and Cloud AI