DevOps & MLOps
Build and operate AI/ML infrastructure. Learn MLOps, CI/CD, Kubernetes, and platform engineering.
MLOps/Platform Engineer deploying and scaling AI systems in production.
- 01MLOps Fundamentals: ML Pipelines, Versioning & Production Infrastructure
- 02CI/CD for AI/ML Pipelines: Automated Testing, Deployment & GitOps
- 03DevSecOps Fundamentals: Security-Integrated CI/CD Pipelines
- 04Platform Engineering: Building Internal Developer Platforms
- 05Kubernetes for AI/ML: Container Orchestration for Production ML
- 06LLM Production Infrastructure: High-Performance Serving & Optimization
The full roadmap.
MLOps Fundamentals: ML Pipelines, Versioning & Production Infrastructure
Master the infrastructure and tools that power production ML systems. Learn data versioning with DVC, workflow orchestration with Kubeflow, feature stores with Feast, model serving with BentoML, and production monitoring with Evidently. Build the foundation for scalable, reproducible machine learning.
CI/CD for AI/ML Pipelines: Automated Testing, Deployment & GitOps
Master continuous integration and deployment for machine learning systems. Learn GitHub Actions and GitLab CI/CD for ML workflows, data validation with Great Expectations and Pandera, experiment automation with DVC and CML, and GitOps deployment with ArgoCD. Build robust, automated pipelines that test, validate, and deploy ML models to production.
DevSecOps Fundamentals: Security-Integrated CI/CD Pipelines
Master security-first DevOps practices. Learn SAST, DAST, SCA, container security, secrets management, and compliance automation with hands-on tools like Snyk, Trivy, Vault, and GitHub Advanced Security.
Platform Engineering: Building Internal Developer Platforms
Master platform engineering with hands-on implementation of Internal Developer Platforms. Learn Backstage for developer portals, Crossplane for infrastructure provisioning, ArgoCD for GitOps, and build self-service golden paths that reduce developer onboarding time by 60%.
Kubernetes for AI/ML: Container Orchestration for Production ML
Master Kubernetes for AI/ML workloads—from GPU scheduling and Kubeflow pipelines to KServe model serving, Istio service mesh, and GitOps with ArgoCD. Learn production-grade orchestration for scalable machine learning.
LLM Production Infrastructure: High-Performance Serving & Optimization
Master LLM inference optimization with vLLM, TensorRT-LLM, and production observability. Learn KV cache management, speculative decoding, LLM gateways, and cost optimization strategies for enterprise-scale AI deployments.
Run a mock interview
Practice with Nerdo as your interviewer. We tailor the questions to this path and grade your performance at the end.
Start with course one.
The first lesson unlocks 50 XP. Most learners finish the opening course in a single weekend.
Start Learning