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ease
features
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Description

ML.NET is a versatile, open-source machine learning framework that is free to use and compatible across platforms, enabling .NET developers to create tailored machine learning models using C# or F# while remaining within the .NET environment. This framework encompasses a wide range of machine learning tasks such as classification, regression, clustering, anomaly detection, and recommendation systems. Additionally, ML.NET seamlessly integrates with other renowned machine learning frameworks like TensorFlow and ONNX, which broadens the possibilities for tasks like image classification and object detection. It comes equipped with user-friendly tools such as Model Builder and the ML.NET CLI, leveraging Automated Machine Learning (AutoML) to streamline the process of developing, training, and deploying effective models. These innovative tools automatically analyze various algorithms and parameters to identify the most efficient model for specific use cases. Moreover, ML.NET empowers developers to harness the power of machine learning without requiring extensive expertise in the field.

Description

A data science platform designed to enhance productivity offers unmatched features that facilitate the development and assessment of superior machine learning (ML) models. By leveraging enterprise-trusted data swiftly, businesses can achieve greater flexibility and meet their data-driven goals through simpler deployment of ML models. Cloud-based solutions enable organizations to uncover valuable business insights efficiently. The journey of constructing a machine learning model is inherently iterative, and this ebook meticulously outlines the stages involved in its creation. Readers can engage with notebooks to either build or evaluate various machine learning algorithms. Experimenting with AutoML can yield impressive data science outcomes, allowing users to create high-quality models with greater speed and ease. Moreover, automated machine learning processes quickly analyze datasets, recommending the most effective data features and algorithms while also fine-tuning models and clarifying their results. This comprehensive approach ensures that businesses can harness the full potential of their data, driving innovation and informed decision-making.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

.NET
Bing
C#
F#
Google Cloud AutoML
Microsoft Defender Antivirus
Microsoft Outlook
Microsoft Power BI
ONNX
Oracle Cloud Infrastructure
TensorFlow

Integrations

.NET
Bing
C#
F#
Google Cloud AutoML
Microsoft Defender Antivirus
Microsoft Outlook
Microsoft Power BI
ONNX
Oracle Cloud Infrastructure
TensorFlow

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Microsoft

Founded

1975

Country

United States

Website

dotnet.microsoft.com/en-us/apps/ai/ml-dotnet

Vendor Details

Company Name

Oracle

Founded

1977

Country

United States

Website

www.oracle.com/data-science/

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Product Features

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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