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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

There is no doubt that data and analytics serve as essential catalysts for revolutionary business projects. An increasing number of individuals throughout organizations are utilizing data to tackle intricate inquiries. The necessity for user-friendly, low-code yet adaptable tools for data transformation and machine learning has reached unprecedented levels. The reliance on multiple disparate tools often results in inefficient analytics workflows, elevated costs, and delayed decision-making processes. Outdated solutions with redundant capabilities pose a risk to ongoing data science endeavors, especially as proprietary features in closed vendor platforms become outdated. By merging extensive expertise in data preparation, machine learning, and visualization into a single cohesive interface, Knowledge Works adapts to expanding data volumes, the introduction of new open-source functionalities, and the evolving sophistication of user profiles. As a result, data scientists and business analysts can seamlessly implement data analytics applications through its accessible, cloud-compatible interface. This integration not only enhances productivity but also fosters a more collaborative environment for data-driven decision-making across the organization.

Description

Azure Machine Learning Studio enables organizations to streamline the entire machine learning lifecycle from start to finish. Equip developers and data scientists with an extensive array of efficient tools for swiftly building, training, and deploying machine learning models. Enhance the speed of market readiness and promote collaboration among teams through leading-edge MLOps—akin to DevOps but tailored for machine learning. Drive innovation within a secure, reliable platform that prioritizes responsible AI practices. Cater to users of all expertise levels with options for both code-centric and drag-and-drop interfaces, along with automated machine learning features. Implement comprehensive MLOps functionalities that seamlessly align with existing DevOps workflows, facilitating the management of the entire machine learning lifecycle. Emphasize responsible AI by providing insights into model interpretability and fairness, securing data through differential privacy and confidential computing, and maintaining control over the machine learning lifecycle with audit trails and datasheets. Additionally, ensure exceptional compatibility with top open-source frameworks and programming languages such as MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R, thus broadening accessibility and usability for diverse projects. By fostering an environment that promotes collaboration and innovation, teams can achieve remarkable advancements in their machine learning endeavors.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

APERIO DataWise
Amazon Web Services (AWS)
Azure Data Science Virtual Machines
Azure Database for MariaDB
Azure Kinect DK
Azure Marketplace
Azure Percept
BotCore
Evvox
Kedro
MLflow
Microsoft Azure
Microsoft Intelligent Data Platform
ModelOp
NVIDIA Triton Inference Server
New Relic
Omnisient
Superwise
Visual Studio Code
Wizata

Integrations

APERIO DataWise
Amazon Web Services (AWS)
Azure Data Science Virtual Machines
Azure Database for MariaDB
Azure Kinect DK
Azure Marketplace
Azure Percept
BotCore
Evvox
Kedro
MLflow
Microsoft Azure
Microsoft Intelligent Data Platform
ModelOp
NVIDIA Triton Inference Server
New Relic
Omnisient
Superwise
Visual Studio Code
Wizata

Pricing Details

No price information available.
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

Altair

Founded

1985

Country

United States

Website

www.altair.com/knowledge-works/

Vendor Details

Company Name

Microsoft

Founded

1975

Country

United States

Website

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

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 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

Alternatives

Alternatives

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