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Ratings and Reviews 0 Ratings
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What is SigmaRed?
Our platform employs a versatile methodology for assessing and tackling risks associated with AI in both models and datasets, emphasizing critical concerns such as bias, proxy bias, and fairness. Through our Responsible AI technology, we improve the transparency of AI models, ensuring they are both understandable and interpretable. Our research-backed algorithms are designed to detect and mitigate risks arising from insufficient robustness. Furthermore, the platform performs comprehensive evaluations of the AI landscape concerning various AI and MRM regulations, providing detailed risk assessments, thorough reporting, and automated remediation solutions. It is essential to scrutinize and reduce AI risks found in both in-house developed systems and those provided by external vendors. The SigmaRed platform streamlines a robust third-party AI risk management (AI TPRM) process, dramatically accelerating the AI risk assessment timeline while enhancing visibility, control, and reporting tailored to stakeholders, alongside maintaining a thorough evidence repository. Additionally, our technology not only bolsters compliance but also builds confidence in AI systems by ensuring proactive management of potential risks, ultimately fostering a safer and more reliable AI environment for all users. This comprehensive approach guarantees that organizations can navigate the complexities of AI regulations effectively while minimizing exposure to vulnerabilities.
What is Azure Machine Learning?
Optimize the complete machine learning process from inception to execution. Empower developers and data scientists with a variety of efficient tools to quickly build, train, and deploy machine learning models. Accelerate time-to-market and improve team collaboration through superior MLOps that function similarly to DevOps but focus specifically on machine learning. Encourage innovation on a secure platform that emphasizes responsible machine learning principles. Address the needs of all experience levels by providing both code-centric methods and intuitive drag-and-drop interfaces, in addition to automated machine learning solutions. Utilize robust MLOps features that integrate smoothly with existing DevOps practices, ensuring a comprehensive management of the entire ML lifecycle. Promote responsible practices by guaranteeing model interpretability and fairness, protecting data with differential privacy and confidential computing, while also maintaining a structured oversight of the ML lifecycle through audit trails and datasheets. Moreover, extend exceptional support for a wide range of open-source frameworks and programming languages, such as MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R, facilitating the adoption of best practices in machine learning initiatives. By harnessing these capabilities, organizations can significantly boost their operational efficiency and foster innovation more effectively. This not only enhances productivity but also ensures that teams can navigate the complexities of machine learning with confidence.
Integrations Supported
APERIO DataWise
Azure AI Search
Azure Container Registry
Azure Data Science Virtual Machines
Azure Database for MariaDB
Azure Kinect DK
Azure Marketplace
Azure Percept
BotCore
Cranium
Integrations Supported
APERIO DataWise
Azure AI Search
Azure Container Registry
Azure Data Science Virtual Machines
Azure Database for MariaDB
Azure Kinect DK
Azure Marketplace
Azure Percept
BotCore
Cranium
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
SigmaRed
Company Website
www.sigmared.ai/
Company Facts
Organization Name
Microsoft
Date Founded
1975
Company Location
United States
Company Website
azure.microsoft.com/en-us/services/machine-learning/
Categories and Features
Categories and Features
Data Labeling
Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
