From foundational DevOps to cutting-edge LLMOps — we provide world-class training, consulting, and certification across every modern IT operations discipline.
Click any domain to learn about our specialized training and consulting programs.
Master CI/CD, IaC, containerization, and monitoring. The complete DevOps lifecycle from code to production.
Site Reliability Engineering — SLO/SLI, error budgets, chaos engineering, and incident management at scale.
Security integrated into CI/CD — SAST, DAST, container scanning, secrets management, zero-trust.
Multi-cloud operations at enterprise scale — AWS, Azure, GCP networking, security, and cost management.
Build Internal Developer Platforms — Backstage, golden paths, self-service portals, developer experience.
Git-driven deployments with ArgoCD and Flux. Declarative infrastructure and progressive delivery.
Cloud financial management — cost allocation, budgeting, reserved instances, showback/chargeback models.
Data pipeline automation, data quality monitoring, and agile data management. Analytics engineering at scale.
AI-powered IT operations — anomaly detection, automated remediation, LLM infrastructure, predictive analytics.
ML model lifecycle management — experiment tracking, model registry, deployment, GPU acceleration, monitoring.
LLM deployment & operations — prompt engineering, RAG pipelines, model serving, cost optimization, guardrails.
Security operations — SIEM, threat detection, incident response, compliance automation, vulnerability management.
IT operations management — ITSM, infrastructure monitoring, CMDB, service desk, event management.
System operations — Linux administration, scripting, automation, system monitoring, performance tuning.
All 15 domains interconnect to form a complete modern IT operations landscape. Our programs cover every layer.
DevOps • GitOps • Platform Engineering — Automate your software delivery pipeline from commit to production.
DevSecOps • SecOps • SRE • SysOps — Run resilient, secure, and observable production systems at any scale.
DataOps • AIOps • MLOps • LLMOps — Operationalize data pipelines and AI/ML models at enterprise scale.
Duration: 3-4 months • 240+ lab hours
Start This PathDuration: 6-8 months • 500+ lab hours
Start This PathDuration: 5-7 months • 400+ lab hours
Start This PathNot sure which path fits your career goals? Talk to our advisors for a personalized learning plan.
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