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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

Description

Elevate your security testing experience, from establishing your setup to examining your data processing outcomes, with esDynamic, which streamlines your efforts and saves you precious time while maximizing the effectiveness of your attack strategies. Explore this adaptable and all-encompassing Python-based platform, expertly designed to support every step of your security evaluations. Tailor your research environment to fit your specific needs by seamlessly incorporating new tools, integrating equipment, and adjusting data. Moreover, esDynamic offers a vast repository of resources on intricate subjects that would usually necessitate considerable research or the input of a specialized team, providing immediate access to expert knowledge. Move away from disorganized data and piecemeal information. Embrace a unified workspace that encourages your team to easily exchange data and insights, enhancing collaboration and speeding up the discovery process. Additionally, consolidate and fortify your work within JupyterLab notebooks for streamlined sharing among your team members, ensuring everyone is on the same page. This holistic approach can significantly transform your security testing workflow.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Python
Amazon SageMaker
Amazon Web Services (AWS)
Apache Spark
Axolotl
Clone Protocol
CogniSync
Flask
Google Cloud Platform
IBM Cloud
Keras
Ludwig
Microsoft Azure
New Relic
PyTorch
ScalePad Backup Radar
Seldon
Ultralytics
ZenML
esChecker

Integrations

Python
Amazon SageMaker
Amazon Web Services (AWS)
Apache Spark
Axolotl
Clone Protocol
CogniSync
Flask
Google Cloud Platform
IBM Cloud
Keras
Ludwig
Microsoft Azure
New Relic
PyTorch
ScalePad Backup Radar
Seldon
Ultralytics
ZenML
esChecker

Pricing Details

$179 per user per month
Free Trial
Free Version

Pricing Details

Free
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

Comet

Founded

2017

Country

United States

Website

www.comet.com

Vendor Details

Company Name

eShard

Country

France

Website

eshard.com/esdynamic

Product Features

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Machine Learning

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

Product Features

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Software Testing

Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing

Alternatives

JupyterLab Reviews

JupyterLab

Jupyter