// the find
alirezadir/Production-Level-Deep-Learning
A guideline for building practical production-level deep learning systems to be deployed in real world applications.
A reference guide for engineers moving ML models from notebooks to production, covering the full pipeline from data labeling through deployment. It's a structured summary of the Full Stack Deep Learning Bootcamp (2019) with tool recommendations for each stage. Aimed at ML engineers or software engineers new to the operational side of ML.
The breadth is useful as a checklist — data versioning, experiment tracking, serving infrastructure, and embedded deployment each get their own section with concrete tool options. The framing around impact vs. cost for prioritizing ML projects is the most actionable part and holds up well. The comparison diagram of ML frameworks by development vs. production suitability is a good at-a-glance reference. Covers topics that are genuinely under-discussed in ML tutorials, like CI/CD for ML and service mesh integration.
The content is frozen in 2019 — TFX and KubeFlow sections are literally '[TBD]', several linked services (FigureEight, Losswise, Floyd, Pipeline.ai) are dead or acquired, and the framework comparison predates PyTorch's dominance in both research and production. There's no code, no working examples, no Jupyter notebooks — it's a glorified list of links with brief summaries. The 'Troubleshooting' section is also '[TBD]', which is arguably the most practically useful topic. Anyone following this guide will immediately run into outdated tool recommendations and will need to cross-reference current state elsewhere.