System Design Masterclass

The core of ML platform engineering: the production gap, packaging, serving, pipelines, feature stores, registries, monitoring, scaling, and production RAG.

Data engineering for ML: batch and streaming, ingestion, ETL and ELT, lakes and warehouses, validation, versioning, labeling, and data contracts.

LLM and GenAI Ops: fine-tuning vs RAG, parameter-efficient tuning, evaluation, prompt management, vector databases, inference optimization, guardrails, and agents.