Online payment fraud is constantly evolving with new attack vectors. Individual businesses lack enough data to build effective models, and overly aggressive fraud rules block legitimate customers, costing merchants revenue.

Stripe Radar leverages the Stripe network effect — training ML models on aggregated signals from millions of businesses processing hundreds of billions of dollars. The system scores every transaction in real-time and adapts to new fraud patterns within hours.

Uber's ML use cases span ETAs, fraud detection, pricing, matching, and safety across hundreds of teams. Without a unified platform, each team built one-off solutions leading to duplicated effort, inconsistent quality, and deployment bottlenecks.

Michelangelo provides an end-to-end platform covering feature engineering (with a centralized Feature Store), model training (supporting XGBoost, deep learning, and custom frameworks), evaluation, deployment, and monitoring — all accessible through a unified API and UI.

Language learning requires conversational practice and personalized feedback, but human tutors don't scale. Traditional chatbots felt robotic and couldn't handle the nuance of language instruction across 40+ languages.

Duolingo built two GPT-4 powered features: 'Explain My Answer' provides contextual grammar explanations for incorrect responses, and 'Roleplay' simulates real-world conversations with AI characters that adapt difficulty to the learner.

With a catalog of thousands of titles across diverse genres and languages, helping each member discover content they'll enjoy — without overwhelming them — is a massive personalization challenge at global scale.

Netflix developed a multi-algorithm recommendation system combining collaborative filtering, content-based methods, deep learning for session-based recommendations, and contextual bandits for real-time optimization of artwork and rankings.

Developers spend significant time writing boilerplate code, looking up APIs, and context-switching between documentation and editors. Code completion tools were limited to simple token-level suggestions.

GitHub partnered with OpenAI to build Copilot, which uses Codex (a GPT descendant fine-tuned on code) to provide multi-line, context-aware code suggestions directly in the IDE, trained on billions of lines of public code.

Businesses need to understand how to effectively implement generative AI across different functions like marketing, customer service, and software development to stay competitive and drive innovation.

Google Cloud provides enterprises with a suite of generative AI solutions and tools that can be applied across various use cases including content generation, customer service, software development, and data analysis.