1000 Models Journey, 500x Scalability and zero downtime k8s migration
🐳 Migrating Large-Scale Compute Workloads to Kubernetes Without Disruption
What happened – Uber moved thousands of interactive ML and batch jobs from Peloton to Kubernetes clusters with zero customer downtime.
Why it matters – Shows a phased, controller-based migration path for stateful compute that unlocks ecosystem tooling and capacity isolation at hyperscale.
🌐 Bringing Connections into View: Real-Time BGP Route Visibility on Cloudflare Radar
What happened – Cloudflare Radar now streams live BGP updates, rendering Sankey diagrams of every prefix’s current AS-path worldwide.
Why it matters – Gives operators instant insight into leaks, hijacks and outages without waiting for historical dumps or running their own collectors.
🚕 Real-Time Spatial Temporal Forecasting @ Lyft
What happened – Lyft built a pipeline forecasting marketplace supply, demand and ETAs every minute for 4 million geohashes using streaming features.
Why it matters – Demonstrates low-latency, fine-grained predictions via hierarchical models and distributed inference, improving pricing and driver dispatch accuracy city-wide.
💰 Implement Event-Driven Invoice Processing for Resilient Financial Monitoring at Scale
What happened – AWS reference architecture ingests 86 million daily invoice events with EventBridge, Lambda and DynamoDB, providing cross-Region replay and stuck-event alerts.
Why it matters – Blueprint lets finance teams gain near-real-time visibility and fault isolation without managing servers or batch reconciliations.
🎥 Behind the Scenes: Building a Robust Ads Event Processing Pipeline
What happened – Netflix designed a Kafka-Flink pipeline handling billions of ad impressions daily with exactly-once guarantees and minute-level campaign feedback.
Why it matters – Offers recipe for low-latency, lossless ad analytics that scales horizontally while keeping costs predictable.
🤖 Journey to 1000 Models: Scaling Instagram’s Recommendation System
What happened – Instagram expanded its ranking stack to orchestrate 1,000 specialized models per request without exceeding existing latency budgets.
Why it matters – Shows sharded model graphs, dynamic gating and GPU inference clusters can unlock personalization depth without compromising experience.
🧪 500× Scalability of Experiment Metric Computing with Unified Dynamic Framework
What happened – Pinterest built a Spark-based framework that computes thousands of experiment metrics in minutes, scaling 500× versus prior Hadoop flows.
Why it matters – Frees engineers to launch more A/B tests and iterate faster while slashing compute cost and pipeline overhead.
🔑 How Agoda Solved Authorization at Scale with OPA
What happened – Agoda centralized policy enforcement via Open Policy Agent sidecars and plugins, serving millions of checks per second across microservices.
Why it matters – Provides playbook for adopting policy-as-code with audit trails and negligible latency, easing compliance and developer onboarding.
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