FreeArtificial Intelligence
MLOps & LLMOps Engineering
Take a model from a notebook to production: experiment tracking and a model registry with MLflow, real deployment, and LLMOps for running LLMs live, then ship a full pipeline from training to monitored production.
The third leg of the AI track: operating models in production. Understand what MLOps is, why models rot without it, and how it extends DevOps to the ML lifecycle.
- What MLOps is and the problems it solves
- The ML lifecycle: data, training, deployment, monitoring
- How MLOps extends DevOps to models and data
- Model drift, retraining and continuous delivery
- The MLOps workflow end-to-end
- Where data engineering, DevOps and ML meet
- Reproducibility, versioning and automation
- The tooling landscape at a glance