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Description

Domino is a comprehensive enterprise AI platform that enables organizations to transform AI initiatives into scalable, production-ready systems. It supports the full AI lifecycle, including data access, model development, deployment, and ongoing management. The platform provides a self-service environment where data scientists can access tools, datasets, and compute resources with built-in governance and security controls. Domino allows teams to build machine learning models, generative AI applications, and intelligent agents using their preferred development environments. It also includes advanced orchestration capabilities to manage workloads across hybrid, multi-cloud, and on-premises infrastructures. Governance features such as model registries, audit trails, and policy enforcement ensure compliance and reproducibility. The platform enhances collaboration by providing a centralized system of record for all AI assets and experiments. Additionally, it helps organizations optimize costs through resource management and usage tracking. Domino is designed to meet enterprise standards for security and regulatory compliance. Ultimately, it empowers businesses to accelerate AI innovation while maintaining operational control and accountability.

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 
H2O.ai Yes 
Amazon EC2 Trn2 Instances Yes 
Flask Yes 
GitHub Yes 
GitLab Yes 
Google Cloud Platform No 
Keras No 
LLaMA-Factory No 
Modulos AI Governance Platform No 
NVIDIA NGC Yes 
Okera Yes 
Ray No 
Snowflake Yes 
TensorFlow No 
TrueFoundry No 
UbiOps No 
lakeFS No 
navio No 
neptune.ai No 

Integrations

Amazon SageMaker Yes 
H2O.ai Yes 
Amazon EC2 Trn2 Instances No 
Flask No 
GitHub No 
GitLab No 
Google Cloud Platform Yes 
Keras Yes 
LLaMA-Factory Yes 
Modulos AI Governance Platform Yes 
NVIDIA NGC No 
Okera No 
Ray Yes 
Snowflake No 
TensorFlow Yes 
TrueFoundry Yes 
UbiOps Yes 
lakeFS Yes 
navio Yes 
neptune.ai Yes 

Pricing Details

Per user/per year
Free Trial Yes 
Free Version No 

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 Yes 
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 Yes 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Domino Data Lab

Founded

2013

Country

United States

Website

www.dominodatalab.com

Vendor Details

Company Name

MLflow

Founded

2018

Country

United States

Website

mlflow.org

Product Features

Artificial Intelligence

Chatbot No 
For Healthcare No 
For Sales No 
For eCommerce No 
Image Recognition No 
Machine Learning No 
Multi-Language No 
Natural Language Processing No 
Predictive Analytics No 
Process/Workflow Automation No 
Rules-Based Automation No 
Virtual Personal Assistant (VPA) No 

Data Management

Customer Data No 
Data Analysis No 
Data Capture No 
Data Integration No 
Data Migration No 
Data Quality Control No 
Data Security No 
Information Governance No 
Master Data Management No 
Match & Merge No 

Data Science

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

Deep Learning

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

Machine Learning

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

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 

Alternatives

Alternatives

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