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Average Ratings 0 Ratings

Total
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

DagsHub serves as a collaborative platform tailored for data scientists and machine learning practitioners to effectively oversee and optimize their projects. By merging code, datasets, experiments, and models within a cohesive workspace, it promotes enhanced project management and teamwork among users. Its standout features comprise dataset oversight, experiment tracking, a model registry, and the lineage of both data and models, all offered through an intuitive user interface. Furthermore, DagsHub allows for smooth integration with widely-used MLOps tools, which enables users to incorporate their established workflows seamlessly. By acting as a centralized repository for all project elements, DagsHub fosters greater transparency, reproducibility, and efficiency throughout the machine learning development lifecycle. This platform is particularly beneficial for AI and ML developers who need to manage and collaborate on various aspects of their projects, including data, models, and experiments, alongside their coding efforts. Notably, DagsHub is specifically designed to handle unstructured data types, such as text, images, audio, medical imaging, and binary files, making it a versatile tool for diverse applications. In summary, DagsHub is an all-encompassing solution that not only simplifies the management of projects but also enhances collaboration among team members working across different domains.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS) Yes 
Google Cloud Platform Yes 
Microsoft Azure Yes 
PyTorch Yes 
Python Yes 
TensorFlow Yes 
Apache Spark Yes 
Clone Protocol Yes 
Flask Yes 
GitHub No 
Google Colab No 
IBM Cloud Yes 
Keras Yes 
Kubeflow No 
Kubernetes No 
Plotly Dash Yes 
R No 
Ultralytics Yes 
Weaviate Yes 
pandas No 

Integrations

Amazon Web Services (AWS) Yes 
Google Cloud Platform Yes 
Microsoft Azure Yes 
PyTorch Yes 
Python Yes 
TensorFlow Yes 
Apache Spark No 
Clone Protocol No 
Flask No 
GitHub Yes 
Google Colab Yes 
IBM Cloud No 
Keras No 
Kubeflow Yes 
Kubernetes Yes 
Plotly Dash No 
R Yes 
Ultralytics No 
Weaviate No 
pandas Yes 

Pricing Details

$179 per user per month
Free Trial No 
Free Version Yes 

Pricing Details

$9 per month
Free Trial Yes 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
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) Yes 
In Person No 

Vendor Details

Company Name

Comet

Founded

2017

Country

United States

Website

www.comet.com

Vendor Details

Company Name

DagsHub

Country

United States

Website

dagshub.com

Product Features

Data Science

Access Control No 
Advanced Modeling No 
Audit Logs No 
Data Discovery No 
Data Ingestion No 
Data Preparation No 
Data Visualization No 
Model Deployment No 
Reports No 

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library Yes 
Model Training Yes 
Neural Network Modeling No 
Self-Learning No 
Visualization Yes 

Machine Learning

Deep Learning Yes 
ML Algorithm Library Yes 
Model Training Yes 
Natural Language Processing (NLP) Yes 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization Yes 

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