Master Data Engineering for AI
A production-grade curriculum for engineers who build the data infrastructure behind AI systems.
Most ML courses skip the hardest part - getting the right data, at the right time, at the right quality. This curriculum teaches you to build the data systems that make ML actually work in production.
The Curriculum
10 modules. From data foundations to real-time feature engineering - the complete data stack for AI.
Data Foundations
Who it's for: Engineers building foundational data processing systems for ML workloads.
| Module | Topics |
|---|---|
| 01 - Data Engineering Foundations | Data landscape, storage formats, schema design, data modeling for ML |
| 02 - Batch Processing | Apache Spark architecture, transformations, joins, partitioning, optimization |
| 03 - Stream Processing | Streaming concepts, Kafka, Flink, windowing, exactly-once semantics |
Data Quality and Storage
Who it's for: Engineers responsible for data reliability and modern storage architectures.
| Module | Topics |
|---|---|
| 04 - Data Quality and Contracts | Quality dimensions, data contracts, Great Expectations, schema enforcement |
| 05 - Feature Stores | Feature store architecture, Feast, offline/online serving, feature sharing |
| 06 - Data Lakehouse | Lake vs. warehouse vs. lakehouse, Delta Lake, Iceberg, Hudi, ACID on object storage |
Orchestration and Platforms
Who it's for: Engineers building and operating data pipelines at scale.
| Module | Topics |
|---|---|
| 07 - Pipeline Orchestration | Airflow architecture, DAG design, task dependencies, retry strategies, monitoring |
| 08 - Cloud Data Platforms | Snowflake, BigQuery, Redshift - architecture, ML integration, cost optimization |
| 09 - Data Observability | Five pillars of observability, lineage tracking, anomaly detection, SLAs |
Real-Time ML
Who it's for: Engineers building real-time feature pipelines for online ML systems.
| Module | Topics |
|---|---|
| 10 - Real-Time Feature Engineering | Online vs. offline features, streaming features, point-in-time correctness, feature freshness |
What You Will Be Able to Do
After completing this curriculum:
- Design data pipelines that feed ML models with clean, versioned, timely data
- Build feature stores that serve features consistently across training and inference
- Implement stream processing for real-time ML systems with exactly-once guarantees
- Enforce data quality with contracts, validation, and automated monitoring
- Architect data lakehouses that combine the best of lakes and warehouses for ML
The Engineering Standard
Every lesson in this curriculum:
- Starts with a real data failure that broke an ML system in production
- Covers architecture and trade-offs - not just tool tutorials
- Includes working code with production patterns
- Ends with design questions that test data systems thinking
This is not a tutorial platform. It is an engineering curriculum.
Career Outcomes
Prepared for roles including:
- Data Engineer (ML/AI)
- ML Platform Engineer
- Analytics Engineer
- Data Infrastructure Engineer
- MLOps Engineer
Certification (Coming Soon)
EngineersOfAI - Data Engineering for AI Certification
Practical. Infrastructure-focused. Production-ready. For engineers who build the data systems that ML depends on - not just the models.