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Average Ratings 0 Ratings

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features
design
support

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Write a Review

Description

Apheris serves as a collaborative platform that allows organizations to work together on distributed data in a manner that is secure, private, and adheres to regulatory standards. By utilizing the Apheris Compute Gateway in conjunction with your data, machine learning and analytics processes occur directly at the data source, preventing any movement or direct accessibility of the data, thereby preserving its inherent value. This innovative methodology resolves common issues associated with data silos that arise from geographical, regulatory, or organizational constraints, as well as situations where data is too sensitive or expensive to transport. Unlike other methods such as synthetic data generation, encryption, or data clean rooms—which may compromise the validity of results, introduce risks of data breaches, or lack scalability—Apheris employs a federated approach to develop models across entire data cohorts without transferring any actual data. With a foundation built on governance, security, and privacy, Apheris guarantees compliance with regulations from the outset, enabling organizations to leverage their data assets more effectively. Ultimately, this unique strategy not only enhances data usability but also instills confidence among stakeholders regarding data protection and regulatory adherence.

Description

MLReef allows domain specialists and data scientists to collaborate securely through a blend of coding and no-coding methods. This results in a remarkable 75% boost in productivity, as teams can distribute workloads more effectively. Consequently, organizations are able to expedite the completion of numerous machine learning projects. By facilitating collaboration on a unified platform, MLReef eliminates all unnecessary back-and-forth communication. The system operates on your premises, ensuring complete reproducibility and continuity of work, allowing for easy rebuilding whenever needed. It also integrates with established git repositories, enabling the creation of AI modules that are not only explorative but also versioned and interoperable. The AI modules developed by your team can be transformed into user-friendly drag-and-drop components that are customizable and easily managed within your organization. Moreover, handling data often necessitates specialized expertise that a single data scientist might not possess, making MLReef an invaluable asset by empowering field experts to take on data processing tasks, which simplifies complexities and enhances overall workflow efficiency. This collaborative environment ensures that all team members can contribute to the process effectively, further amplifying the benefits of shared knowledge and skill sets.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Docker No 
Keras No 
MXNet No 
PyTorch No 
TensorFlow No 
Ubuntu No 
scikit-image No 

Integrations

Docker Yes 
Keras Yes 
MXNet Yes 
PyTorch Yes 
TensorFlow Yes 
Ubuntu Yes 
scikit-image Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux Yes 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Apheris

Country

Germany

Website

www.apheris.com

Vendor Details

Company Name

MLReef

Country

United States

Website

www.mlreef.com

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

Alternatives

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