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🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, Apptainer, and more.

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and... Read more

From idea to production in just few lines: Graph-Based Programmable Neuro-Symbolic LM Framework - a production-first LM framework built with decade old Deep Learning best practices

RusTorch is a production-grade deep learning framework re-imagined in Rust. It combines the usability you love from PyTorch with the performance, safety, and concurrency guarantees of Rust. Say goodbye to GIL locks, GC pauses, and runtime errors. Say hello to RusTorch.

This MLOps project productionizes a Deep Reinforcement Learning agent with a scalable, distributed data streaming infrastructure using Kafka and Ray. A thorough walkthrough of the code is described in this article on medium: https://ryanraymartin.medium.com/deep-reinforcement-learning-for-stock-t... Read more