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This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

This skill covers configuring and managing object storage solutions like AWS S3, MinIO, and GCS. It includes CLI operations for bucket management, uploading/downloading files, setting up versioning, implementing bucket policies for access control, and configuring lifecycle rules for cost optimization. Suitable for DevOps engineers managing application assets, backups, logs, and media files in cloud or on-premises environments.

Designs the smallest useful expense claim setup for a business: policy limits, approval routing, cost centres and budget lines. Produces CSV, SQL DDL, JSON Schema or Notion templates on request for claim approvals and monthly reconciliation.