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Average Ratings 0 Ratings

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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

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 Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker Yes 
Apache Spark Yes 
Axolotl Yes 
Google Cloud Platform Yes 
Keras Yes 
Ludwig Yes 
TensorFlow Yes 
ZenML Yes 
CogniSync Yes 
Determined AI No 
H2O.ai No 
HoneyHive No 
Kubernetes No 
LLaMA-Factory No 
Python Yes 
TrueFoundry No 
Union Cloud No 
Vectice No 
conDati No 
navio No 

Integrations

Amazon SageMaker Yes 
Apache Spark Yes 
Axolotl Yes 
Google Cloud Platform Yes 
Keras Yes 
Ludwig Yes 
TensorFlow Yes 
ZenML Yes 
CogniSync No 
Determined AI Yes 
H2O.ai Yes 
HoneyHive Yes 
Kubernetes Yes 
LLaMA-Factory Yes 
Python No 
TrueFoundry Yes 
Union Cloud Yes 
Vectice Yes 
conDati Yes 
navio Yes 

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 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

Comet

Founded

2017

Country

United States

Website

www.comet.com

Vendor Details

Company Name

MLflow

Founded

2018

Country

United States

Website

mlflow.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 

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 

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