Building Scalable AI-Powered Platforms
summary
A microservices CPQ platform is built end to end: AI models for configuration and dynamic pricing, event-sourced pipelines, feature store and online features, GitOps deployments and multi‑cloud Terraform modules. Measured claims include 100k monthly requests, 99.9% uptime, median 200 millisecond response for pricing and 50 ms inference for ML serving. Tools and patterns are concrete (event-driven streams, autoscaling with custom ML metrics, canary/blue‑green GitOps, feature consistency) and case studies report 10× capacity growth and 65% faster time to market.
AI-written summary · about
chapters (12)
- 0:00Introduction and Speaker Background
- 0:35Leveraging AI in CPQ Systems
- 1:57AI-Powered CPQ Architecture
- 2:43Real-Time Data and Dynamic Pricing
- 4:10Microservices and Response Time
- 6:52Integration Patterns and Event-Driven Communication
- 10:25Multi-Cloud Infrastructure and Scalability
- 14:17Developer Experience and Tooling
- 17:19Observability and Incident Response
- 22:15Data Platform Integration and Security
- 27:38Performance Optimization Strategies
- 29:02Case Studies and Conclusion
related talks
14mPerformance Optimization in High-Volume Transaction Systems
21mHarnessing Microservices for Scalable, Fault-Tolerant ML Systems
25mAI-Powered O11y in Microservices Architectures
20mAccelerating innovation using tiered AI Architecture
08mMicroservices at Scale
18mScaling AI Agents With gRPC: Intuit's Enterprise MCP Service Marketplace
42mInnovating Faster with Micro-Service Architecture...
59mArchitecting Microservices for Agentic AI Integration