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Free lessons: GenAI Data Engineering
35 lessons free to read — no subscription required. Open any one to read the full lesson; subscribe to unlock the rest. Browse all 35 by course ↓
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What you'll build in GenAI Data Engineering
Signature lessons unique to this discipline — start here.
Foundation
The same four core lessons on every hub — shared across all twelve disciplines.
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Every free lesson on this path, course by course, in curriculum order.
Python Essentials for Agent Builders1 lesson
LLM Foundations for Agent Builders1 lesson
Kubernetes Essentials for GenAI Engineers1 lesson
Web APIs & Services for GenAI Engineers1 lesson
GenAI Data Pipelines31 lessons
- Ch 1Extract documents using Docling's unified multi-format parser3 free labs
- Ch 1Use Google Document AI for managed OCR and layout parsing
- Ch 1Design a unified document model normalizing all extraction outputs3 free labs
- Ch 1Build a routing system selecting the optimal extraction method3 free labs
- Ch 2Implement content quality scoring with NeMo Curator filters
- Ch 3Build Anthropic's Contextual Retrieval pattern
- Ch 4Extract structured metadata using Instructor with Pydantic schemas
- Ch 5Design multi-format storage strategies on GCS and PostgreSQL
- Ch 6Build an embedding benchmarking framework
- Ch 6Evaluate Voyage 4's shared embedding space across model tiers
- Ch 6Implement an embedding abstraction layer with provider switching
- Ch 7Build embedding pipelines with LiteLLM gateway routing
- Ch 7Track costs in real-time with Langfuse and enforce budgets
- Ch 7Orchestrate pipelines as Argo Workflows with Kafka triggers
- Ch 8Configure AlloyDB with pgvector and ScaNN indexing
- Ch 8Build zero-downtime reindexing for embedding model upgrades
- Ch 9Build semantic caching using Redis LangCache
- Ch 10Route extraction by complexity to cost-effective models
- Ch 12Build agentic RAG with query decomposition and self-verification
- Ch 13Build automated maintenance workflows with Argo
- Ch 14Instrument pipelines with OpenTelemetry GenAI conventions
- Ch 15Implement Presidio regex and NER-based PII detection
- Ch 15Add NeMo Curator PII redaction for pipeline-scale detection
- Ch 15Deploy NeMo Guardrails for output safety and validation
- Ch 16Build event-driven triggers with Kafka and KEDA autoscaling
- Ch 16Version datasets with DVC backed by GCS
- Ch 16Connect pipeline agents via MCP for autonomous orchestration
- Ch 16Implement pipeline observability with OTel, Prometheus, Grafana
- Ch 17Implement model cascading for cost reduction
- Ch 18Design end-to-end architecture on GKE Autopilot
- Ch 18Deploy infrastructure with Crossplane + Helm + Kustomize
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