Related Products
|
||||||
About
Flower is an open source federated learning framework designed to simplify the development and deployment of machine learning models across decentralized data sources. It enables training on data located on devices or servers without transferring the data itself, thereby enhancing privacy and reducing bandwidth usage. Flower supports a wide range of machine learning frameworks, including PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and XGBoost, and is compatible with various platforms and cloud services like AWS, GCP, and Azure. It offers flexibility through customizable strategies and supports both horizontal and vertical federated learning scenarios. Flower's architecture allows for scalable experiments, with the capability to handle workloads involving tens of millions of clients. It also provides built-in support for privacy-preserving techniques like differential privacy and secure aggregation.
|
About
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.
|
|||||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
|||||
Audience
Machine learning practitioners and researchers in search of a tool to implement privacy-preserving, decentralized model training across diverse devices and platforms
|
Audience
Organizations interested in a powerful open source machine learning platform
|
|||||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
|||||
API
Offers API
|
API
Offers API
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
Free
Free Version
Free Trial
|
Pricing
Free
Free Version
Free Trial
|
|||||
Reviews/
|
Reviews/
|
|||||
Pros & Cons from Real UsersPros
Cons
|
||||||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||||
Company InformationFlower
Founded: 2023
Germany
flower.ai/
|
Company InformationTensorFlow
Founded: 2015
United States
www.tensorflow.org
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
||||||
|
|
|
|||||
|
|
||||||
Categories |
Categories |
|||||
Machine Learning Features
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
|
||||||
Integrations
JAX
Keras
Python
Amazon EC2 Trn2 Instances
Amazon Elastic Inference
Amazon SageMaker Studio
Cerebrium
Gemini Enterprise Agent Platform Notebooks
GigaSpaces
Jupyter Notebook
|
Integrations
JAX
Keras
Python
Amazon EC2 Trn2 Instances
Amazon Elastic Inference
Amazon SageMaker Studio
Cerebrium
Gemini Enterprise Agent Platform Notebooks
GigaSpaces
Jupyter Notebook
|
|||||
|
|
|