Capability
MLOps & Model Deployment
Getting models and LLM features into production and keeping them honest: evaluation, cost and latency tracking, human review, and deploys that can be rerun safely.
Problems I Solve
Where this capability fits
A model or LLM feature works in a notebook, but deploys, monitoring, and rollback are an afterthought.
LLM spend is untracked until the bill arrives, and traffic is unpredictable.
Generated output that reaches the public needs a human check before it publishes.
Health checks say the job ran, not whether the data or the model output is current.
Scope
Capabilities
Per-call token and cost logging with hard daily caps
Evaluation on real examples before a threshold goes live
Human-in-the-loop review queues and approval screens
Containerized services with idempotent deploy scripts
Scheduled jobs with overlap locks, run ledgers, and backups
Classical ML: anomaly detection, PCA, k-means clustering
Health endpoints that report live data coverage
Example Systems
Systems I design and build
LLM features with cost ceilings and answer reuse
Review and publishing workflows for generated content
Anomaly detection over regional and financial data
Model and pipeline scheduling on a single server or in the cloud
Technical Stack
Tools and platforms
Need this capability on your product?
Start with a technical scoping call focused on data sources, users, and success criteria.