Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
You can import findings from more than 20 popular security and pentesting tools and present them in a variety of formats, including Word, Excel and HTML. Multiple methodologies can be used for different stages of a project. This will allow you to keep track of all your tasks, and ensure consistent results throughout your organization. It is easier to work together when security project data, tool outputs and scope, results, screenshots, and notes are all centralized. To keep everyone on the same page, track changes, give feedback and push out updated findings, you can track them all. You don't need to learn new technologies. Simply combine the outputs from your favorite security tools, such as Nessues and Burp, Nmap, and more to create custom reports. Our simple, yet powerful templates will help you create reports in a matter of minutes, not days. Dradis Gateway can help you overcome the limitations of static security reports. You can share the results of security assessments in real time.
Description
MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Cisco Duo
Yes
Comet LLM
No
Databricks
No
Determined AI
No
Docker
No
Flyte
No
Invicti
Yes
Jozu
No
Microsoft 365
No
Microsoft Azure
Yes
Integrations
Cisco Duo
No
Comet LLM
Yes
Databricks
Yes
Determined AI
Yes
Docker
Yes
Flyte
Yes
Invicti
No
Jozu
Yes
Microsoft 365
Yes
Microsoft Azure
No
Pricing Details
$79 per month
Per user per month
billed annually
billed annually
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
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
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Dradis Framework
Founded
2010
Country
United Kingdom
Website
dradis.com
Vendor Details
Company Name
MLflow
Founded
2018
Country
United States
Website
mlflow.org
Product Features
Collaboration
Brainstorming
No
Calendar Management
Yes
Chat / Messaging
Yes
Contact Management
Yes
Content Management
Yes
Document Management
Yes
Project Management
Yes
Real Time Editing
Yes
Task Management
Yes
Version Control
Yes
Video Conferencing
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