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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.
Description
Visdom serves as a powerful visualization tool designed to create detailed visual representations of real-time data, assisting researchers and developers in monitoring their scientific experiments conducted on remote servers. These visualizations can be accessed through web browsers and effortlessly shared with colleagues, fostering collaboration. With its interactive capabilities, Visdom is tailored to enhance the scientific experimentation process. Users can easily broadcast visual representations of plots, images, and text, making it accessible for both personal review and team collaboration. The organization of the visualization space can be managed via the Visdom user interface or through programmatic means, enabling researchers and developers to thoroughly examine experiment outcomes across various projects and troubleshoot their code. Additionally, features such as windows, environments, states, filters, and views offer versatile options for managing and viewing critical experimental data. Ultimately, Visdom empowers users to build and tailor visualizations specifically suited for their projects, streamlining the research workflow. Its adaptability and range of features make it an invaluable asset for enhancing the clarity and accessibility of scientific data.
API Access
Has API
API Access
Has API
Integrations
Aporia
Azure Machine Learning
Azure Marketplace
Cranium
Databricks Data Intelligence Platform
Docker
Flyte
Google Cloud Platform
Keras
Ludwig
Integrations
Aporia
Azure Machine Learning
Azure Marketplace
Cranium
Databricks Data Intelligence Platform
Docker
Flyte
Google Cloud Platform
Keras
Ludwig
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
MLflow
Founded
2018
Country
United States
Website
mlflow.org
Vendor Details
Company Name
Meta
Country
United States
Website
ai.meta.com/tools/visdom/
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization