ClinchWorks
Explore a subject, work through the curriculum, and choose your next step.
Implement eight guided product-inspired projects with system design, Docker, Kubernetes, infrastructure as code, CI/CD, security, observability and optional AI. Approximately 96 core practice hours plus optional cloud extensions; complete applications are learner-built.
Build a non-root containerized application, run it in a dedicated local Kubernetes cluster, and develop evidence-based delivery, security, scaling, troubleshooting, recovery, and maintenance skills. Twelve teaching modules lead to a local operational capstone and twenty-question final assessment; conditional infrastructure exercises state their prerequisites.
Make AI systems reproducible, observable, and maintainable. Expanded engineering lessons with complete local practice, worked solutions, failure cases and instructor assessment.
Build exact vector retrieval, measure approximation failures, implement persisted metadata-filtered CRUD, and diagnose retrieval errors with labeled evaluation. Includes metric calculations, a coarse-list ANN reference, and optional real SentenceTransformer and Qdrant workflows.
Integrate RAG, agents, MCP, fine-tuning, serving, evaluation, and production engineering into one portfolio-grade AI system. Expanded engineering lessons with complete local practice, worked solutions, failure cases and instructor assessment.
Build reliable structured tool execution for LLM applications. Expanded engineering lessons with worked examples, runnable local practice, failure investigation and evidence-based assessment.
