Technology
ML infrastructure, MLOps pipelines, model registries, observability, and engineering maturity for production AI.
Do you have a scalable cloud or hybrid infrastructure capable of supporting ML workloads?
Are MLOps or AI engineering pipelines established for model lifecycle management?
Is there a feature store or reusable ML feature repository in place?
Do you use CI/CD practices for AI/ML model deployment?
Are model monitoring and drift detection systems operational?
Is there a model registry with versioning and audit trails?
Do you have GPU/specialized compute provisioned or on-demand access?
Are AI development environments standardized and reproducible?
Is there API gateway / integration layer for AI services?
Do you employ A/B testing or shadow deployment for AI models?
Is there observability tooling (logging, tracing) for AI services in production?
Does your tech stack support real-time inference at scale?