Master MLOps. Build production ML systems that last.
Curated, no-fluff curriculum from real-world experience. Tools change, fundamentals don't.
The next generation of best-prepared innovators are MLOps engineers
Why MLOps engineers?
Bridge theory and production
MLOps engineers uniquely combine machine learning expertise with systems engineering, turning research into real-world impact.
Master complexity at scale
They navigate the full ML lifecycle—from data pipelines to model deployment—building systems that work reliably at scale.
Drive business outcomes
By ensuring models perform in production, MLOps engineers directly connect technical work to measurable business value.
Future-proof skills
As AI adoption accelerates, the demand for engineers who can operationalize ML systems continues to grow exponentially.
- Data versioning & lineage
- Experiment tracking
- Reproducibility
- Pipelines (batch/stream)
- Training orchestration
- CI/CD for ML
- Monitoring & drift
- Governance & risk
- Cost & SLOs
ls -lahfind . -name "*.py" -type fgrep -r "pattern" /path/to/dirtail -f /path/to/logfilecp -r source/ destination/du -sh * | sort -hBuilding Robust Microservices for Data-Intensive Applications
Learn to architect and deploy microservices that efficiently handle large-scale data processing, ensuring scalability, reliability, and performance.
- Streaming vs batch processing
- Event-driven architectures
- Data partitioning strategies
- Message queue patterns
- Horizontal scaling patterns
- Caching strategies
- Database optimization
- Load balancing & routing
- Circuit breakers & retries
- Fault tolerance patterns
- Distributed tracing
- Graceful degradation
- Polyglot persistence
- Data consistency models
- Schema evolution
- Data lifecycle management
- Metrics & alerting
- Log aggregation
- Performance profiling
- Data quality monitoring
- Data encryption at rest & in transit
- Access control & authentication
- Compliance & audit trails
- Data privacy patterns
All current and future content. One subscription.