Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Machine learning reveals concealed patterns and valuable insights within enterprise data, ultimately adding significant value to businesses. Oracle Machine Learning streamlines the process of creating and deploying machine learning models for data scientists by minimizing data movement, incorporating AutoML technology, and facilitating easier deployment. Productivity for data scientists and developers is enhanced while the learning curve is shortened through the use of user-friendly Apache Zeppelin notebook technology based on open source. These notebooks accommodate SQL, PL/SQL, Python, and markdown interpreters tailored for Oracle Autonomous Database, enabling users to utilize their preferred programming languages when building models. Additionally, a no-code interface that leverages AutoML on Autonomous Database enhances accessibility for both data scientists and non-expert users, allowing them to harness powerful in-database algorithms for tasks like classification and regression. Furthermore, data scientists benefit from seamless model deployment through the integrated Oracle Machine Learning AutoML User Interface, ensuring a smoother transition from model development to application. This comprehensive approach not only boosts efficiency but also democratizes machine learning capabilities across the organization.
API Access
Has API
No
API Access
Has API
Yes
Integrations
.NET
Yes
Apache Hive
No
Apache Spark
No
Bing
Yes
C#
Yes
F#
Yes
Google Cloud AutoML
Yes
Impala
No
Kinetica
No
Microsoft Defender Antivirus
Yes
Integrations
.NET
No
Apache Hive
Yes
Apache Spark
Yes
Bing
No
C#
No
F#
No
Google Cloud AutoML
No
Impala
Yes
Kinetica
Yes
Microsoft Defender Antivirus
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Deployment
Web-Based
No
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
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/machine-learning/
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
Product Features
Data Science
Access Control
No
Advanced Modeling
No
Audit Logs
No
Data Discovery
No
Data Ingestion
No
Data Preparation
No
Data Visualization
No
Model Deployment
No
Reports
No
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