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ease
features
design
support

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Description

The Kaizen Framework is an efficient low-code tool designed for rapid application development, allowing users to create web applications in a matter of minutes. It significantly reduces development costs and minimizes reliance on developers, eliminating the need for coding, compilation, and downtime for users. True to its name, "Kaizen," the framework is consistently updated to ensure that you can maximize your profitability in software development projects. As a mature option among various low-code frameworks, Kaizen has demonstrated its effectiveness by delivering a wide range of applications across multiple industries. Over the past 15 years, it has evolved continuously, resulting in the successful execution of over 500 projects in more than 70 different sectors, providing highly practical solutions. Additionally, it is user-friendly, making it straightforward to learn and deploy, with the flexibility to host applications wherever you choose. With advanced features that set it apart from competitors, the Kaizen Framework is an ideal choice for those looking to streamline their development process.

Description

Developing machine learning applications should be effortless and seamless. UnionML is an open-source framework in Python that enhances Flyte™, streamlining the intricate landscape of ML tools into a cohesive interface. You can integrate your favorite tools with a straightforward, standardized API, allowing you to reduce the amount of boilerplate code you write and concentrate on what truly matters: the data and the models that derive insights from it. This framework facilitates the integration of a diverse array of tools and frameworks into a unified protocol for machine learning. By employing industry-standard techniques, you can create endpoints for data retrieval, model training, prediction serving, and more—all within a single comprehensive ML stack. As a result, data scientists, ML engineers, and MLOps professionals can collaborate effectively using UnionML apps, establishing a definitive reference point for understanding the behavior of your machine learning system. This collaborative approach fosters innovation and streamlines communication among team members, ultimately enhancing the overall efficiency and effectiveness of ML projects.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) No 
Google Cloud Platform No 
Microsoft Azure No 
Python No 

Integrations

Amazon Web Services (AWS) Yes 
Google Cloud Platform Yes 
Microsoft Azure Yes 
Python Yes 

Pricing Details

$10 USD per user per day
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App Yes 
Windows Yes 
Mac No 
Linux Yes 
Chromebook Yes 

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 No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs No 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Virtual Splat Software

Founded

2000

Country

India

Website

kaizenframework.app/

Vendor Details

Company Name

Union

Founded

2021

Country

United States

Website

www.union.ai/unionml

Product Features

Low-Code Development

AI-Assisted Development No 
Business Process Automation No 
Collaborative Development No 
Data Aggregation and Publishing No 
Deployment Management No 
Drag & Drop No 
Integrations Management No 
Iteration Management No 
Performance Monitoring No 
Requirements Management No 
Templates No 
Visual Modeling No 
Web / Mobile App Development No 
Workflow Management No 

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 

Alternatives

Alternatives

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