BURT CONSULTANTS
Topic

mlops

6 pieces tagged “mlops”.

Briefing

Assembling a cost-efficient AI infrastructure stack layer by layer.

Spheron maps AI infrastructure into seven layers. Understanding each layer helps teams buy only what they need and avoid duplicating capabilities.

aiinfrastructurearchitecturecost
2 min read
Briefing

A production checklist for Kubernetes GPU workloads.

Running GPU workloads on Kubernetes requires more than scheduling pods. The Good Shell's checklist covers drivers, partitioning, queues, Spot GPUs and autoscaling.

kubernetesgpumlopsinfrastructure
2 min read
Briefing

A pragmatic ladder for adopting Kubernetes-native MLOps.

KodeKloud's 2026 consensus stack shows how Kubernetes-native tools can bring MLOps into production. The trick is to adopt them in stages rather than all at once.

kubernetesmlopsinfrastructurekubeflow
2 min read
Briefing

Hiring for AI in 2026: why MLOps engineers are the new unicorns.

AI hiring is shifting from research roles to production engineering. MLOps specialists are now the hardest people to recruit, and the gap says a lot about where value really sits.

aimlopshiringinfrastructure
3 min read
Briefing

Why production LLM serving is now a senior-tier specialisation.

Senior MLOps engineers increasingly command a premium because LLM serving requires deep knowledge of inference architecture, cost optimisation and scalable infrastructure.

llmmlopsinferencecost
2 min read
Briefing

MLOps in 2026: from experiments to production value

Eight MLOps trends are reshaping how organisations move AI from experiment to production: pipeline automation, cloud-native operations, real-time AI, observability, governance and edge deployment.

aimlopsinfrastructureengineering
2 min read

Browse all tags →