alirezadir/Production-Level-Deep-Learning
A guideline for building practical production-level deep learning systems to be deployed in real world applications.
Covers the complete ML system lifecycle beyond model training—including data management (labeling, versioning, feature stores), workflow orchestration (Airflow, Luigi), and infrastructure decisions. Provides curated tooling recommendations across the full stack: object storage (S3, Ceph), databases (Postgres), data versioning (DVC, Pachyderm), and training frameworks, alongside best practices for project scoping and cost-benefit analysis in ML initiatives.
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