This repository serves as a curated collection of notes, references, and tutorials for deploying deep learning models into production environments. It targets engineers and researchers involved in MLOps, aiming to provide a comprehensive guide to various frameworks, tools, and best practices for model serving, optimization, and deployment across different platforms.

How It Works

The repository organizes resources by deep learning framework (PyTorch, TensorFlow, Keras, MXNet), deployment target (web, mobile, embedded), and supporting technologies (serving frameworks, containerization, MLOps tools). It highlights conversion techniques between frameworks (e.g., ONNX), optimization strategies (quantization, pruning), and infrastructure considerations (Kubernetes, AWS Lambda).

Quick Start & Requirements

This repository is a collection of links and notes, not a runnable project. No installation or execution commands are provided.

Highlighted Details

• Extensive coverage of PyTorch and TensorFlow deployment patterns, including C++ APIs and serving with Flask/TorchServe.
• Resources on model conversion and interoperability using ONNX and MMdnn.
• Detailed sections on mobile/embedded deployment (ncnn, TensorFlow Lite) and containerized/orchestrated deployments (Docker, Kubernetes, Kubeflow, Seldon Core).
• Focus on performance optimization techniques like quantization, pruning, and hardware acceleration (NVIDIA Triton, TensorRT, OpenVINO).

Maintenance & Community

The repository is maintained by ahkarami. No specific community channels or active development signals are present in the README.

Licensing & Compatibility

The repository itself contains links to various open-source projects, each with its own license. The content is for informational purposes and does not impose a specific license on the user's projects.

Limitations & Caveats

This is a curated list of external resources, not a unified framework or tool. Users must individually evaluate and integrate the linked projects. The content may not reflect the latest advancements or best practices in the rapidly evolving MLOps landscape.