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What is Microsoft Fabric?

Integrating all data sources with analytics services into a unified AI-driven platform will revolutionize the way individuals access, manage, and utilize data along with the insights derived from it. With all your data and teams consolidated in one location, collaboration becomes seamless. Develop a centralized lake-centric hub that empowers data engineers to link various data sources and curate them effectively. This approach will reduce data sprawl while enabling the creation of tailored views for diverse user needs. By fostering the advancement of AI models without the need to transfer data, analysis can be accelerated, significantly cutting down the time required for data scientists to produce valuable insights. Tools like Microsoft Teams, Microsoft Excel, and other Microsoft applications can significantly enhance your team's ability to innovate rapidly. Facilitate responsible connections between people and data with a flexible, scalable solution that enhances the control of data stewards, bolstered by its inherent security, compliance, and governance features. This innovative framework encourages collaboration and promotes a culture of data-driven decision-making across the organization.

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.

Media

Media

Integrations Supported

Azure Marketplace
Microsoft Intelligent Data Platform
Amazon S3
BIChart
FLIP
Google Analytics
Microsoft Dataverse
Microsoft Excel
Microsoft Teams
Milo
Mindfuel
Salesforce
Unity Catalog

Integrations Supported

Azure Marketplace
Microsoft Intelligent Data Platform
APERIO DataWise
Azure Percept
Cranium
Kedro
NVIDIA Triton Inference Server
New Relic

API Availability

API Availability

Pricing Information

$156.334/month/2CU
Prices vary depending on the geographic region and capacity units.
There are two ways to get started: pay-as-you-go or by reservation.
Free Trial Offered?

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

www.microsoft.com/en-us/microsoft-fabric

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/products/machine-learning/

Categories and Features

AI Data Analytics

Not specified

AI Development

Not specified

Data Engineering

Not specified

Data Fabric

Not specified

Data Integration

Not specified

Semantic Layer

Not specified

Categories and Features

AI Development

Not specified

AI Governance

Not specified

AI Infrastructure

Not specified

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

Predictive Modeling

ML Model Deployment

Not specified

ML Model Management

Not specified

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