Open source platform for the machine learning lifecycle
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Updated
Jun 12, 2024 - Python
Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
Open source platform for the machine learning lifecycle
An Open Source Machine Learning Framework for Everyone
Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.
Substrate Python SDK
A curated list of awesome Machine Learning frameworks, libraries and software. With repository stars⭐ and forks🍴
Visualizer for neural network, deep learning and machine learning models
The platform for customizing AI from enterprise data
Friendli: the fastest serving engine for generative AI
Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
Front end system for the grocery saving platform (supersavers.au). Supersavers is platform that can help save on groceries by comparing prices at Coles, Woolworths and IGA. The backend was implemented using python and ML techniques and is currently kept confidential
Eternal is an experimental platform for machine learning models and workflows.
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
🤖 Bring the magic of ChatGPT to Google Search (powered by Google Gemma + GPT-4o!)
🤖 Apps that utilize the astounding power of ChatGPT or enhance its UX
Production Grade Nifi & Nifi Registry. Deploy for VM (Virtual Machine) with Terraform + Ansible, Helm & Helmfile for Kubernetes (EKS)
Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, Gemma, CLIP, ViT, ConvNeXt, BEiT, Swin Transformer, Segformer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
MINERS ⛏️: The semantic retrieval benchmark for evaluating multilingual language models.