Chapter 12: Deployment & MLOps for Scientific Applications

https://leanpub.com/generativeaiforscience

Introduction: From Notebooks to Production Science

https://leanpub.com/generativeaiforscience

Part I: Experiment Tracking & Management

https://leanpub.com/generativeaiforscience

The Problem: Experiment Chaos

https://leanpub.com/generativeaiforscience

MLflow for Scientific Experiments

https://leanpub.com/generativeaiforscience

Part II: Data Versioning & Lineage

https://leanpub.com/generativeaiforscience

DVC for Scientific Data

https://leanpub.com/generativeaiforscience

Part III: Model Lifecycle Management

https://leanpub.com/generativeaiforscience

Model Registry for Science

https://leanpub.com/generativeaiforscience

Part IV: Continuous Training Pipelines

https://leanpub.com/generativeaiforscience

Automated Retraining with New Data

https://leanpub.com/generativeaiforscience

Part V: Scientific Validation & Testing

https://leanpub.com/generativeaiforscience

Domain-Specific Test Suite

https://leanpub.com/generativeaiforscience

Part VI: Deployment to Scientific Infrastructure

https://leanpub.com/generativeaiforscience

Integration with HPC Clusters

https://leanpub.com/generativeaiforscience

Part VII: Monitoring Production Models

https://leanpub.com/generativeaiforscience

Scientific Drift Detection

https://leanpub.com/generativeaiforscience

References

https://leanpub.com/generativeaiforscience

Up next

Chapter 13: Future Directions & Conclusion