Why this track exists
AI models are only as good as the data they receive. Building a real AI system means building the infrastructure that cleans, transforms, enriches, and delivers data — reliably, at scale, and in a form that models can actually use.
This track covers the engineering layer that most AI tutorials skip entirely.
What you will learn
- Data pipeline design: batch vs streaming, reliability, observability
- Apache Spark and Databricks for large-scale data processing
- Feature engineering: creating the signals that make models accurate
- Vector databases: how semantic search and RAG systems store and retrieve knowledge
- Data quality, validation, and monitoring at scale
Who this is for
- Engineers building ML pipelines or AI-powered products
- Data engineers who want to understand the AI context for their work
- AI engineers who want to go deeper than "just call the API"
What this unlocks
- Applied AI — semantic search, RAG, and AI agents all depend on well-structured data pipelines