Add lineage and reproducibility to pipelines
Attribute inference cost across tenants
Build and operate model serving infrastructure
Build reproducible automated ML pipelines as code
Configure inference providers and gateways
Create ML infrastructure as code with Terraform
Define inference SLOs and alerting
Deploy and operate ML pipelines
Detect model and data drift
Ensure graceful degradation under load
Execute rollout strategies and safe rollback
Handle GPU and accelerator scheduling
Implement autoscaling for inference services
Implement guardrails and prompt version management
Improve on call ergonomics and runbook culture
Manage Kubernetes workloads on GKE
Manage inference latency and throughput
Manage model registry versioning and promotion
Monitor ML reliability and observability
Operate GitOps for ML workloads with ArgoCD
Operate LLM and agentic workloads in production
Optimize ML inference cost and capacity
Right size accelerators and manage committed use and Spot capacity