Connecting Kafka to machine learning models requires careful consideration of latency, scalability, and data consistency requirements.

Kafka Streams integration:

• Use Kafka Streams for real-time stream processing that directly feeds Kafka topics to downstream ML models
• Implement real-time feature engineering and data preparation within the streaming pipeline
• Enable immediate model inference and prediction serving

Microservices architecture:

• Set up microservices that consume Kafka events and use AI/ML frameworks like TensorFlow or PyTorch
• Implement containerized model serving for scalability and isolation
• Use API gateways and load balancers for reliable model access

ML platform integration:

• Leverage integrations between Kafka and platforms like Databricks, MLflow, or Kubeflow
• Seamlessly connect event streams to machine learning model training and serving infrastructure
• Implement MLOps practices for model versioning, monitoring, and deployment

These patterns enable real-time AI applications powered by Snowplow's behavioral data streams.