What is Domino Enterprise MLOps Platform?

The Domino Enterprise MLOps Platform enhances the efficiency, quality, and influence of data science on a large scale, providing data science teams with the tools they need for success. With its open and adaptable framework, Domino allows experienced data scientists to utilize their favorite tools and infrastructures seamlessly. Models developed within the platform transition to production swiftly and maintain optimal performance through cohesive workflows that integrate various processes. Additionally, Domino prioritizes essential security, governance, and compliance features that are critical for enterprise standards.

The Self-Service Infrastructure Portal further boosts the productivity of data science teams by granting them straightforward access to preferred tools, scalable computing resources, and a variety of data sets. By streamlining labor-intensive DevOps responsibilities, data scientists can dedicate more time to their core analytical tasks, enhancing overall efficiency.

The Integrated Model Factory offers a comprehensive workbench alongside model and application deployment capabilities, as well as integrated monitoring, enabling teams to swiftly experiment and deploy top-performing models while ensuring high performance and fostering collaboration throughout the entire data science process.

Finally, the System of Record is equipped with a robust reproducibility engine, search and knowledge management tools, and integrated project management features that allow teams to easily locate, reuse, reproduce, and build upon existing data science projects, thereby accelerating innovation and fostering a culture of continuous improvement. As a result, this comprehensive ecosystem not only streamlines workflows but also enhances collaboration among team members.

Pricing

Price Overview:
Per user/per year
Free Trial Offered?:
Yes

Integrations

Offers API?:
Yes, Domino Enterprise MLOps Platform provides an API

Screenshots and Video

Company Facts

Company Name:
Domino Data Lab
Date Founded:
2013
Company Location:
United States
Company Website:
www.dominodatalab.com
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Product Details

Deployment
SaaS
On-Prem
Training Options
Documentation Hub
Webinars
On-Site Training
Video Library
Support
Standard Support
Web-Based Support

Product Details

Target Company Sizes
1001-5000
5001-10000
10001+
Target Organization Types
Enterprise
Government
Supported Languages
English

Domino Enterprise MLOps Platform Categories and Features

Machine Learning Software

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Deep Learning Software

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

Data Science Software

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

More Domino Enterprise MLOps Platform Categories

Domino Enterprise MLOps Platform Customer Reviews

Write a Review
  • Reviewer Name: Navneet N.
    Position: Software Developer
    Has used product for: 1-2 Years
    Uses the product: Weekly
    Org Size (# of Employees): 100 - 499
    Feature Set
    Layout
    Ease Of Use
    Cost
    Customer Service
    Would you Recommend to Others?
    1 2 3 4 5 6 7 8 9 10

    DDSP

    Date: Dec 18 2020
    Summary

    Overall, if you want a notebook-based environment, this will suit you the best. It is powerful and easy to use with tons of great features.

    Positive

    Domino Data Science has the following pros:
    - Great Customer service.
    - Flexibility with notebooks and the ability to define your environment as per your needs.
    - Collaboration is very much easy with this software.
    - Launches Quickly and easily scalable of Kubernetes infrastructure.

    Negative

    The only cons that I found with this software were that the datasets are not accessible outside the platform. Other than that, this works fine for me.

    Read More...
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