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What is TAZI?

TAZI focuses on delivering concrete business outcomes and enhancing return on investment through predictive insights powered by artificial intelligence. Its user-friendly platform caters to a diverse range of business professionals, from analysts to top executives, ensuring accessibility for all. With the TAZI Profiler, users can swiftly understand and extract valuable insights from their machine learning-ready data sources. Furthermore, TAZI offers business dashboards and explanatory models that aid in comprehending and validating AI models before their implementation. The platform is also adept at pinpointing and predicting various operational segments to boost ROI effectiveness. In addition, it simplifies the evaluation of data quality and the extraction of essential statistics by automating the often time-consuming processes of data discovery and preparation. TAZI enhances the feature engineering process by providing recommendations, including those for intricate features and data transformations, enabling users to effectively tap into their data's full capabilities. Ultimately, TAZI stands out as a holistic solution for businesses eager to harness the power of AI to drive success. The platform not only facilitates informed decision-making but also encourages a data-driven culture within organizations.

What is MLBox?

MLBox is a sophisticated Python library tailored for Automated Machine Learning, providing a multitude of features such as swift data ingestion, effective distributed preprocessing, thorough data cleansing, strong feature selection, and precise leak detection. It stands out with its capability for hyper-parameter optimization in complex, high-dimensional environments and incorporates state-of-the-art predictive models for both classification and regression, including techniques like Deep Learning, Stacking, and LightGBM, along with tools for interpreting model predictions. The main MLBox package is organized into three distinct sub-packages: preprocessing, optimization, and prediction, each designed to fulfill specific functions: the preprocessing module is dedicated to data ingestion and preparation, the optimization module experiments with and refines various learners, and the prediction module is responsible for making predictions on test datasets. This structured approach guarantees a smooth workflow for machine learning professionals, enhancing their productivity. In essence, MLBox streamlines the machine learning journey, rendering it both user-friendly and efficient for those seeking to leverage its capabilities.

Media

Media

Integrations Supported

GitHub
Python

Integrations Supported

GitHub
Python

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

TAZI

Date Founded

2015

Company Location

United States

Company Website

www.tazi.ai/

Company Facts

Organization Name

Axel ARONIO DE ROMBLAY

Date Founded

2017

Company Website

mlbox.readthedocs.io/en/latest/

Categories and Features

Machine Learning

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

Categories and Features

Machine Learning

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

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