Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, real-time data pipelines. In modern ML systems, stream processing enables models to react to data as it is generated, rather than waiting for nightly batch jobs. Use cases include fraud detection, recommendation engines, live anomaly detection, and real-time feature serving. Kafka decouples data producers from consumers, letting each scale independently. Events are persisted on disk, so consumers can replay history or catch up after downtime. Understanding Kafka is essential for any ML engineer building low-latency, always-on inference pipelines in production.
Analogy🏏Cricket
🏏 Think of it like cricket: Evidently AI is the IPL's official analytics platform — rather than each franchise building their own stats system, they use a shared platform that automatically computes every standardized metric: batting averages, economy rates, strike rates, net run rates. When Virat Kohli's performance drifts from his baseline, the platform highlights it automatically with charts. Evidently does the same for ML models: instead of each team coding their own drift detectors, they use Evidently's pre-built metrics and get standardized, comparable reports automatically. The standardization is the strategic point, not a convenience: because every franchise reads the same metric definitions, a drift score of 0.3 means the same thing in every dashboard, reports can be compared across teams and seasons, and a new analyst is productive on day one. Hand-rolled monitoring scripts fail exactly here — every team's 'drift check' quietly means something different, and nobody can audit whose alarm was right.