Tag

ML engineering

Pipelines and Predictions: Making AI a First-Class Citizen in Your SDLC

Aaron Wise

Integrating AI/ML into the SDLC requires treating models as production code, with shared ownership, CI/CD pipelines, and cross-functional teams.

The MLOps Playbook: Automating, Optimizing, and Scaling ML Workflows

Aaron Wise

MLOps scales ML workflows by combining automation, versioning, and observability to balance cost, performance, and reliability.

Data Engineering vs. ML Engineering: Bridging the Data-to-Insight Divide

Aaron Wise

Data engineers build reliable data pipelines while ML engineers deploy models, but both must collaborate closely to bridge the data-to-insight divide.