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A practical, Bash-first roadmap to become production-ready in AI on Linux: master shell and system tools, build isolated Python workstations, start DL on CPU then add NVIDIA GPU, enforce reproducibility with Git and containers (Podman/Docker), automate with tmux/systemd/cron, scale with PySpark/Dask, and ship portfolio-ready pipelines—plus apt/dnf/zypper commands and copyable workflows, with next steps like DDP, MLflow, and Airflow.