Lilly Tech Systems
Kafka for ML Pipelines
Master real-time streaming data for machine learning — from Kafka fundamentals and streaming ML to feature pipelines and real-time inference systems.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why streaming matters for ML, event-driven architectures, and where Kafka fits in the ML ecosystem.
2. Kafka Basics
Topics, partitions, producers, consumers, consumer groups, and the Kafka architecture.
3. Streaming ML
Connecting Kafka to ML frameworks, Kafka Streams for feature computation, and windowed aggregations.
4. Feature Pipelines
Build real-time feature pipelines with Kafka, compute streaming features, and sink to feature stores.
5. Real-time Inference
Deploy ML models for real-time predictions using Kafka consumers, scaling, and monitoring strategies.
6. Best Practices
Schema management, exactly-once semantics, monitoring, testing, and production deployment patterns.
What You'll Learn
By the end of this course, you'll be able to:
Stream Processing
Process real-time event streams with Kafka for machine learning feature computation and inference.
Feature Pipelines
Build real-time feature pipelines that compute and serve features with low latency.
Real-time Inference
Deploy ML models for real-time predictions triggered by streaming events.
Production Systems
Design reliable, scalable ML streaming systems with monitoring and exactly-once semantics.