Building an AI-ready pipeline with source-available components creates a flexible, scalable foundation for machine learning and AI applications.

Data collection and streaming:

• Integrate Snowplow for comprehensive behavioral data collection across all customer touchpoints
• Use Apache Kafka for real-time streaming of event data to AI/ML systems
• Implement proper schema validation and data quality assurance for reliable AI training data

Data processing and transformation:

• Use dbt for data transformation and feature engineering within your data warehouse
• Store raw and enriched data in scalable storage solutions like S3, Azure Data Lake, or Google Cloud Storage
• Implement data versioning and lineage tracking for reproducible AI/ML experiments

ML/AI integration:

• Use MLflow or TensorFlow for model training, versioning, and deployment
• Ensure seamless data flow between data processing and AI/ML components
• Implement Apache Spark or Databricks for large-scale model training on Snowplow data
• Enable real-time inference by feeding processed data into machine learning models

This architecture provides the foundation for sophisticated AI applications while maintaining control over your data and infrastructure.