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
The SensiML Analytics Toolkit enables the swift development of smart IoT sensor devices while simplifying the complexities of data science. It focuses on creating compact algorithms designed to run on small IoT endpoints instead of relying on cloud processing. By gathering precise, traceable, and version-controlled datasets, it enhances data integrity. The toolkit employs advanced AutoML code generation to facilitate the rapid creation of autonomous device code. Users can select their preferred interface and level of AI expertise while maintaining full oversight of all algorithm components. It also supports the development of edge tuning models that adapt behavior based on incoming data over time. The SensiML Analytics Toolkit automates every step necessary for crafting optimized AI recognition code for IoT sensors. Utilizing an expanding library of sophisticated machine learning and AI algorithms, the overall workflow produces code capable of learning from new data, whether during development or after deployment. Moreover, non-invasive applications for rapid disease screening that intelligently classify multiple bio-sensing inputs serve as essential tools for aiding healthcare decision-making processes. This capability positions the toolkit as an invaluable resource in both tech and healthcare sectors.
API Access
Has API
No
API Access
Has API
No
Integrations
.NET
Yes
Bing
Yes
C#
Yes
F#
Yes
Google Cloud AutoML
Yes
Microsoft Defender Antivirus
Yes
Microsoft Outlook
Yes
Microsoft Power BI
Yes
ONNX
Yes
TensorFlow
Yes
Integrations
.NET
No
Bing
No
C#
No
F#
No
Google Cloud AutoML
No
Microsoft Defender Antivirus
No
Microsoft Outlook
No
Microsoft Power BI
No
ONNX
No
TensorFlow
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
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
Yes
Windows
Yes
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
SensiML
Founded
2017
Country
United States
Website
sensiml.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
Product Features
Machine Learning
Deep Learning
Yes
ML Algorithm Library
Yes
Model Training
Yes
Natural Language Processing (NLP)
No
Predictive Modeling
Yes
Statistical / Mathematical Tools
Yes
Templates
Yes
Visualization
Yes