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What is Ludwig?

Ludwig is a specialized low-code platform tailored for crafting personalized AI models, encompassing large language models (LLMs) and a range of deep neural networks. The process of developing custom models is made remarkably simple, requiring merely a declarative YAML configuration file to train sophisticated LLMs with user-specific data. It provides extensive support for various learning tasks and modalities, ensuring versatility in application. The framework is equipped with robust configuration validation to detect incorrect parameter combinations, thereby preventing potential runtime issues. Designed for both scalability and high performance, Ludwig incorporates features like automatic batch size adjustments, distributed training options (including DDP and DeepSpeed), and parameter-efficient fine-tuning (PEFT), alongside 4-bit quantization (QLoRA) and the capacity to process datasets larger than the available memory. Users benefit from a high degree of control, enabling them to fine-tune every element of their models, including the selection of activation functions. Furthermore, Ludwig enhances the modeling experience by facilitating hyperparameter optimization, offering valuable insights into model explainability, and providing comprehensive metric visualizations for performance analysis. With its modular and adaptable architecture, users can easily explore various model configurations, tasks, features, and modalities, making it feel like a versatile toolkit for deep learning experimentation. Ultimately, Ludwig empowers developers not only to innovate in AI model creation but also to do so with an impressive level of accessibility and user-friendliness. This combination of power and simplicity positions Ludwig as a valuable asset for those looking to advance their AI projects.

What is ConfigOS?

ConfigOS has been implemented in both classified and unclassified environments, spanning tactical and weapon system applications, isolated research labs, and commercial cloud settings. This cutting-edge solution functions without requiring client software, thus removing the necessity for software agent installation. ConfigOS rapidly scans endpoint systems and can address hundreds of STIG controls in under 90 seconds. It also provides automated rollback options during remediation, as well as comprehensive compliance reports and outputs from the STIG Viewer Checklist. Built for efficiency, ConfigOS can strengthen every CAT 1/2/3 STIG control based on a specific application baseline in around 60 minutes, which drastically shortens the time required for RMF accreditation, reducing it from the usual weeks or months. The platform is compatible with various Microsoft Windows workstation and server operating systems, along with SQL Server, IIS, Internet Explorer, Chrome, and all Microsoft Office components. In addition, it supports Red Hat versions 5, 6, and 7, as well as SUSE, Ubuntu, and Oracle Linux. With a rich library of over 10,000 STIG and CIS controls, ConfigOS guarantees extensive coverage across a multitude of platforms. Moreover, the recent updates to the Command Center introduce a patent-pending technology that significantly enhances its operational capabilities, making it a versatile tool for compliance management. This positions ConfigOS as a leading solution in the ever-evolving landscape of cybersecurity.

Media

Media

Integrations Supported

Aim
Alpaca
Comet
Discord
Docker
Hugging Face
Kubernetes
Llama 2
MLflow
Python
RAY
TensorBoard
Triton
Weights & Biases

Integrations Supported

AWS Marketplace

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

Linux

Supported Platforms

SaaS
Windows

Customer Service / Support

Not specified

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Uber AI

Date Founded

2016

Company Location

United States

Company Website

ludwig.ai/latest/

Company Facts

Organization Name

SteelCloud

Date Founded

1987

Company Location

United States

Company Website

www.steelcloud.com

Categories and Features

Machine Learning

Not specified

Categories and Features

Compliance

Audit Management
Compliance Tracking
Controls Testing
Risk Management

Cybersecurity

Endpoint Management
Incident Management
Vulnerability Scanning

Endpoint Protection

Activity Log
Application Security
Encryption
Signature Matching
Web Threat Management

Network Security

Access Control
Threat Response
VPN
Vulnerability Scanning

Vulnerability Management

Network Scanning
Policy Management
Risk Management
Vulnerability Assessment

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