“Model inference”

Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers.

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama…

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence.

Plan and coordinate the deployment of a model to Amazon SageMaker AI.

Generates and updates secure, production-ready Kubernetes YAML manifests optimized for GKE Autopilot and GKE Standard clusters.

Reference tables for Copilot Studio YAML authoring: triggers, actions, variables, entities, Power Fx functions, templates.

Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging…

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub.

Pick the right serving container for a SageMaker model deployment and find its current image URI.

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA.

Selects, deploys, and customizes AI models on Amazon SageMaker.

Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl.

Set up AI Runway on AKS — from bare cluster to running model.

Troubleshoots and debugs AWS Clean Rooms collaboration issues related to IAM roles, S3 bucket policies, KMS keys, Lake Formation permissions, and CloudWatch…

Debug live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation.