AI Operations

# AI Operations: Engineering Intelligence at Scale AI Operations (AIOps) represents the critical bridge between cutting-edge AI research and real-world impact. It's not enough to build powerful models - we must deploy, monitor, and maintain them in production environments where reliability and performance are paramount. ## Core Components 1. Model Lifecycle Management 2. Infrastructure Orchestration 3. Performance Monitoring 4. Continuous Learning Systems 5. Governance and Compliance ## Technical Challenges Moving from notebook to production requires containerization, API design, edge deployment strategies, and choosing appropriate inference patterns. Production AI systems need robust data handling including versioning, quality monitoring, and privacy preservation. ## Research Focus I'm exploring efficient operational and procedural modalities for new co piloting and ai enabled ways of being / living. integrating core human needs, stochastic bounds of existence (basicalyl we just fkn vibe end to end as a human and oscilalte moods or cravings or thoughts) to implement and action on things (eg idea to implementation). strategicalyl set ourselves up for intrefaces and life design integrating ai enabled things. transcirption, transforming thoughts, pipelining things to diff projects andl livingdocs for externalizing, and just vibe and resaerch and discuss things. human-in-the-loop operations, and cross-human state tool startegies