A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
-
Updated
May 25, 2024
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Keras implementation of the renowned publication "DeepFace: Closing the Gap to Human-Level Performance in Face Verification" by Taigman et al. Pre-trained weights on VGGFace2 dataset.
Materials for "Machine Learning on Big Data" course
Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo
Carefully curated list of awesome data science resources.
A fully adaptive, zero-tuning parameter manager that enables efficient distributed machine learning training
SOTA Google's Perceiver-AR Music Transformer Implementation and Model
Execution framework for multi-task model parallelism. Enables the training of arbitrarily large models with a single GPU, with linear speedups for multi-gpu multi-task execution.
[DEPRECEATED] Piano Transformer model trained on 2.6GB of MIDI piano music
A comprehensive guide designed to empower readers with advanced strategies and practical insights for developing, optimizing, and deploying scalable AI models in real-world applications.
[DEPRECEATED] Multi-Instrumental Music Transformer trained on 12GB/400k MIDIs
Keras implementation of the renowned publication "FaceNet: A Unified Embedding for Face Recognition and Clustering" by Schroff et al.
This project is for developing a deep neural networks and its variant from scratch. No external libraries are used except for GPU operations.
This is the official codebase for KDD 2021 paper Generalized Zero-Shot Extreme Multi-Label Learning
Our full length research is finally acknowledged through double-blind review procedure.
This repository is a code sample to serve Large Language Models (LLM) on a Google Kubernetes Engine (GKE) cluster with GPUs running NVIDIA Triton Inference Server with FasterTransformer backend.
Official Code Base for ICLR 2024 paper Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction
Crack SWE (ML) / DS MAANG Interviews
Add a description, image, and links to the large-scale-machine-learning topic page so that developers can more easily learn about it.
To associate your repository with the large-scale-machine-learning topic, visit your repo's landing page and select "manage topics."