Frequently Asked Questions
What is the goal of the MLOps & LLMOps track?
This track teaches how to move models from development to deployment. You’ll master automated CI/CD for ML, drift detection, model versioning, and infrastructure orchestration tailored for both traditional ML and modern LLM workflows.
How is LLMOps different from MLOps?
LLMOps extends MLOps practices to large language models. It focuses on managing prompts, handling scale, monitoring hallucinations, and versioning LLM behaviors—all while ensuring security and consistency.
What tools and platforms are covered?
You’ll work with MLflow, DVC, KServe, Terraform, Helm, and Kubernetes. Each tool is covered in context—helping you build auditable, resilient, and secure AI systems.
How does this track support production-readiness?
By focusing on reproducibility and infrastructure-as-code, this track equips engineers to scale AI while maintaining traceability and compliance. It emphasizes strategies for rollback, drift response, and cross-environment parity.