Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Data Version Control (DVC) is an open-source system specifically designed for managing version control in data science and machine learning initiatives. It provides a Git-like interface that allows users to systematically organize data, models, and experiments, making it easier to oversee and version various types of files such as images, audio, video, and text. This system helps structure the machine learning modeling process into a reproducible workflow, ensuring consistency in experimentation. DVC's integration with existing software engineering tools is seamless, empowering teams to articulate every facet of their machine learning projects through human-readable metafiles that detail data and model versions, pipelines, and experiments. This methodology promotes adherence to best practices and the use of well-established engineering tools, thus bridging the gap between the realms of data science and software development. By utilizing Git, DVC facilitates the versioning and sharing of complete machine learning projects, encompassing source code, configurations, parameters, metrics, data assets, and processes by committing the DVC metafiles as placeholders. Furthermore, its user-friendly approach encourages collaboration among team members, enhancing productivity and innovation within projects.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Amazon SageMaker
Yes
Apache Spark
Yes
Axolotl
Yes
Clone Protocol
Yes
CogniSync
Yes
Flask
Yes
Git
No
IBM Cloud
Yes
Keras
Yes
Ludwig
Yes
Integrations
Amazon SageMaker
No
Apache Spark
No
Axolotl
No
Clone Protocol
No
CogniSync
No
Flask
No
Git
Yes
IBM Cloud
No
Keras
No
Ludwig
No
Pricing Details
$179 per user per month
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
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
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
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
Yes
Live Training (Online)
No
In Person
Yes
Vendor Details
Company Name
Comet
Founded
2017
Country
United States
Website
www.comet.com
Vendor Details
Company Name
iterative.ai
Founded
2018
Country
United States
Website
dvc.org
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