The-AI-Summer/Deep-Learning-In-Production
Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
Covers the complete ML lifecycle with hands-on tutorials spanning data pipelines, custom TensorFlow training loops, model serving via Flask/uWSGI/Nginx, and containerization with Docker and Kubernetes. Bridges the gap between research and production by teaching software engineering practices—unit testing, logging, debugging, and code structuring—applied specifically to deep learning projects. Includes distributed training patterns, cloud deployment on Google Cloud/Vertex AI, and MLOps pipeline architecture through practical examples rather than theory.
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May 01, 2023
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