Belajar ML Engineer

Belajar ML Engineer dari nol hingga production-grade: pre-requisites skill & setup environment, peran & perbedaan dengan data scientist, siklus hidup & arsitektur ML, Python for ML production, feature engineering & transformations, training & validation best practices, experiment tracking & registry, model evaluation & metrics, versioning code data & model, containerization & serving, CI/CD untuk ML, model monitoring & drift, deep learning dengan PyTorch, transformers & LLM fundamentals, efficient fine-tuning LoRA/QLoRA, serving LLMs vLLM/TGI, RAG & knowledge systems, data pipelines untuk ML, ML security, model governance & compliance, privacy-preserving ML, distributed training, serving at scale, model optimization, advanced evaluation & eval-driven dev, AI agents & systems engineering, ekosistem & tren modern 2026, hingga roadmap karir & refleksi akhir dengan total 28 episode.