#other #ai #data_science #devops #engineering #federated_learning #machine_learning #ml #mlops #software_engineering
https://github.com/visenger/awesome-mlops
https://github.com/visenger/awesome-mlops
GitHub
GitHub - visenger/awesome-mlops: A curated list of references for MLOps
A curated list of references for MLOps . Contribute to visenger/awesome-mlops development by creating an account on GitHub.
#python #algorithm #fate #federated_learning #machine_learning #privacy_preserving
https://github.com/FederatedAI/FATE
https://github.com/FederatedAI/FATE
GitHub
GitHub - FederatedAI/FATE: An Industrial Grade Federated Learning Framework
An Industrial Grade Federated Learning Framework. Contribute to FederatedAI/FATE development by creating an account on GitHub.
#python #data_analysis #differential_privacy #federated_learning #homomorphic_encryption #machine_learning #privacy_preserving #private_set_intersection #secure_multiparty_computation #split_learning #trusted_execution_environment
https://github.com/secretflow/secretflow
https://github.com/secretflow/secretflow
GitHub
GitHub - secretflow/secretflow: A unified framework for privacy-preserving data analysis and machine learning
A unified framework for privacy-preserving data analysis and machine learning - secretflow/secretflow
#python #federated_analytics #federated_learning #federated_learning_framework #fleet_intelligence #fleet_learning #flower #keras_federated_learning #pytorch_federated_learning #tensorflow_federated_learning
https://github.com/adap/flower
https://github.com/adap/flower
GitHub
GitHub - adap/flower: Flower: A Friendly Federated AI Framework
Flower: A Friendly Federated AI Framework. Contribute to adap/flower development by creating an account on GitHub.
#python #blockchain #deep_learning #distributed_training #edge_ai #federated_learning #inference_engine #machine_learning #marketplace #mlops #on_device_training #privacy #security
https://github.com/FedML-AI/FedML
https://github.com/FedML-AI/FedML
GitHub
GitHub - FedML-AI/FedML: FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated…
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs o...
#cplusplus #federated_learning #fl #hacktoberfest #mpc #multi_party_computation #pir #privacy_preserving #private_information_retrieval #private_set_intersection #psi #security_protocol
https://github.com/primihub/primihub
https://github.com/primihub/primihub
GitHub
GitHub - primihub/primihub: Privacy-Preserving Computing Platform 由密码学专家团队打造的开源隐私计算平台，支持多方安全计算、联邦学习、隐私求交、匿踪查询等。
Privacy-Preserving Computing Platform 由密码学专家团队打造的开源隐私计算平台，支持多方安全计算、联邦学习、隐私求交、匿踪查询等。 - primihub/primihub
#other #ai #data_science #devops #engineering #federated_learning #machine_learning #ml #mlops #software_engineering
This resource is a comprehensive guide to Machine Learning Operations (MLOps), providing a wide range of tools, articles, courses, and communities to help you manage and deploy machine learning models effectively.
**Key Benefits** Access to numerous books, articles, courses, and talks on MLOps, machine learning, and data science.
- **Community Support** Detailed guides on workflow management, feature stores, model deployment, testing, monitoring, and maintenance.
- **Infrastructure Tools** Resources on model governance, ethics, and responsible AI practices.
Using these resources, you can improve your skills in designing, training, and running machine learning models efficiently, ensuring they are reliable, scalable, and maintainable in production environments.
https://github.com/visenger/awesome-mlops
This resource is a comprehensive guide to Machine Learning Operations (MLOps), providing a wide range of tools, articles, courses, and communities to help you manage and deploy machine learning models effectively.
**Key Benefits** Access to numerous books, articles, courses, and talks on MLOps, machine learning, and data science.
- **Community Support** Detailed guides on workflow management, feature stores, model deployment, testing, monitoring, and maintenance.
- **Infrastructure Tools** Resources on model governance, ethics, and responsible AI practices.
Using these resources, you can improve your skills in designing, training, and running machine learning models efficiently, ensuring they are reliable, scalable, and maintainable in production environments.
https://github.com/visenger/awesome-mlops
GitHub
GitHub - visenger/awesome-mlops: A curated list of references for MLOps
A curated list of references for MLOps . Contribute to visenger/awesome-mlops development by creating an account on GitHub.
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