A practical guide for adding a first ML inference step to an existing Kafka-based streaming pipeline without rebuilding the entire platform. Covers the distinction between batch training and real-time inference, a four-step canonical pattern (ingest, enrich, score, produce), common use cases like fraud detection and recommendations, key trade-offs around latency and memory, and four pitfalls to avoid such as building a feature store prematurely or automating retraining too early. The recommended approach is incremental: prove one high-value inference function first, then layer on complexity like model registries and feature stores as the pipeline matures.
Table of contents
Starting With a Single ML Function (Not a Full ML Platform)What Streaming ML Looks Like in PracticeA 4-Step Flow: Simple, Effective Streaming Architectures for Event-Driven MLStep by Step: How to Add Your First ML FunctionCommon First Use CasesDesign Trade-Offs to Know Up FrontWhat NOT to Do for Your First Streaming ML ProjectHow This Fits Into a Larger ML Platform LaterStart Building Real-Time Inference PipelinesStreaming ML – Frequently Asked QuestionsSort: