Databricks Specialization on Coursera
Courses 1, 3 & 4 of the Databricks Specialization on Coursera
Platform: Databricks Free Edition | Comparison Layer: Sovereign AI Stack (Rust)
Design Philosophy
Course 1 is Databricks-only, building the foundation for the specialization.
Courses 3 & 4 use a dual-layer pedagogy:
- Databricks layer — Hands-on with MLflow, Feature Store, Model Serving, Vector Search, Foundation Models
- Sovereign AI Stack layer — Build the same concepts from scratch in Rust to understand what platforms abstract
Why both?
- Practitioners need to use Databricks effectively
- Engineers need to understand what's underneath
- "Understand by building" creates deeper retention
Course Overview
| Course | Title | Duration |
|---|---|---|
| 1 | Lakehouse Fundamentals | ~15 hours |
| 3 | MLOps Engineering | ~30 hours |
| 4 | GenAI Engineering | ~34 hours |
Sovereign AI Stack
┌──────────────────────────────────────────────────────────────────┐
│ batuta (Orchestration) │
│ Privacy Tiers · CLI · Stack Coordination │
├───────────────────┬──────────────────┬───────────────────────────┤
│ realizar │ entrenar │ pacha │
│ (Inference) │ (Training) │ (Model Registry) │
│ GGUF/SafeTensors │ autograd/LoRA │ Sign/Encrypt/Lineage │
├───────────────────┴──────────────────┴───────────────────────────┤
│ aprender │
│ ML Algorithms: regression, trees, clustering │
├──────────────────────────────────────────────────────────────────┤
│ trueno │
│ SIMD/GPU Compute (AVX2/AVX-512/NEON, wgpu) │
├──────────────────────────────────────────────────────────────────┤
│ trueno-rag │ trueno-db │ alimentar │ pmat │
│ BM25 + Vector │ GPU Analytics │ Arrow/Parquet │ Quality │
└──────────────────┴─────────────────┴───────────────┴─────────────┘
Prerequisites
Databricks
- Create a free account at databricks.com
- No paid features required
Sovereign AI Stack (Rust)
# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
# Key crates
cargo install batuta realizar pmat
Getting Started
Begin with Course 1: Lakehouse Fundamentals for the foundational concepts, then continue to Course 3: MLOps Engineering or jump directly to Course 4: GenAI Engineering if you're already familiar with MLOps concepts.