Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Learn how to build knowledge graph memory for AI agents — extracting entities and relationships from text, storing them in graph structures, and querying connected information for richer agent reasoning.
Deploy and scale AI agent services on Kubernetes with Deployments, Services, Horizontal Pod Autoscalers, resource limits, and health checks for production-grade reliability.
Learn how to manage AI agent configuration with Kubernetes ConfigMaps and Secrets — including environment injection, volume mounts, secret rotation, and best practices for API key management.
Master core Kubernetes concepts every AI engineer needs — Pods, Deployments, Services, and ReplicaSets — with practical YAML manifests for deploying AI agent workloads to production clusters.
Use Kubernetes Jobs and CronJobs to run batch AI agent workloads — including parallel document processing, scheduled report generation, and completion tracking with backoff policies.
Design Kubernetes Network Policies to secure AI agent communication — including namespace isolation, egress restrictions to LLM APIs, and deny-all defaults with explicit allow rules.
Build a Kubernetes Operator for AI agent lifecycle management using Custom Resource Definitions, reconciliation loops, and status management to automate agent provisioning and scaling.
Learn how to use Kubernetes Persistent Volumes, PersistentVolumeClaims, and StorageClasses to manage stateful AI agent workloads including vector stores, conversation logs, and model caches.
Implement real-time streaming in LangChain applications with callback handlers for token-by-token output, custom event logging, cost tracking, and production monitoring hooks.
