What's inside the playbook
You'll learn:
A recommendation model trained on last night’s batch is making decisions about a customer who existed six hours ago, and by the time it catches up, the offer that would have converted has become noise. This playbook covers the full real-time pipeline (event capture, streaming ingestion, enrichment, feature store, inference, activation) with named tools, real benchmarks, and the trade-offs you’ll actually face in production.
- The six-stage architecture that gets you from customer signal to served recommendation in under 100 milliseconds
- Behavioral event capture across web, mobile, server-side, IoT, and offline channels, with identity stitched at ingestion
- Feature store selection: Redis, vector databases, warehouse-plus-cache patterns, and when semantic caching cuts inference cost by 86%
- Hybrid model architectures (collaborative filtering, content-based, two-tower neural networks) and why production systems combine multiple approaches
- Serving patterns for scale: continuous batching, model quantization (FP32 to FP16 to FP8 to INT4), and disaggregated serving as used by Netflix and Spotify
- The business and system metrics that actually predict recommendation ROI, and the drift and feedback loop failures to catch early
- Real-time consent enforcement as a circuit breaker, not a compliance checkbox, with governance patterns that survive an audit
- What agentic AI, generative recommendation content, and multi-modal signals mean for the pipeline you're building today
A recommendation model trained on last night’s batch is making decisions about a customer who existed six hours ago, and by the time it catches up, the offer that would have converted has become noise. This playbook covers the full real-time pipeline (event capture, streaming ingestion, enrichment, feature store, inference, activation) with named tools, real benchmarks, and the trade-offs you’ll actually face in production.
