Home/New SaaS Software/Determined AI vs MLflow
Updated on: February 7, 2023

Compare Determined AI vs MLflow

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Overview

Summary

Determined AI is a hyperparameter tuning and distributed deep learning platform, enabling users to generate profound learning models as per their convenience. It is specially configured for developers’ productivity increment, resource (GPU) utilization improvement and risk reduction. The open-source platform allows users to train models within a short time span i.e hours and minutes. Difficult tasks like re-running faulty jobs, hyperparameter tuning, hardware resource management etc is taken care of by Determined AI, allowing users to focus on other important business tasks. The distributed training module within Determined AI is designed to outperform industrial standards and needs no alterations within code. It is also integrated with the state of the art training platform built within the software. An experiment dashboard, accompanied by a sophisticated checkpoint and resource scheduling mechanism, enables users to share their experiments, resources and data with their team members in a secured manner. The software offers real-time compatibility with deep learning frameworks like Keras, PyTorch and more. ..read more

MLflow is a futuristic machine learning platform that helps developers manage the entire machine learning lifecycle in an efficient manner. In addition, it also helps users to proceed with experimentation, deployment, and reproducibility of codes. Currently, MLflow offers four components to individual users. The first component is Tracking, which helps developers with logging parameters, metrics, code versions, and output files while running the machine learning code and result analytics part. The second component is Projects that include a command-line and other API tools facilitating seamless management of projects in progress. This also makes it possible for developers to bring all projects with a single workflow. The third component being Models, which can be used for packaging machine learning models, besides proceeding with deployment in diverse serving environments. Finally, Registry- a centralized model serves as a store for APIs and UI, besides helping out users to proceed with the full life cycle of an MLflow Model. Real-time integration facilities with Kubernetes, Google Cloud, and TensorFlow, enables seamless business management. ..read more

Features

TECHNICAL DETAILS

Access Monitoring

24/7 (Live rep)

Business Hours

Online

Contact Number/Address

San Francisco, California

San Francisco, California

API

Deployment

SaaS/Web/Cloud

Mobile - Android

Mobile - iOS

Mobile - Windows

Mobile - BlackBerry

Installed - Windows

Installed - Mac

Customers

Individuals

Freelancers

Large Enterprises

Medium Business

Small Business

Pricing

Pricing Model

Free Trial

Freemium

One-time license

Open-source

Subscription

Quotation Based

Plans

Determined AI
Custom

Features

  • AutoML at scale
  • Seamless infrastructure
  • Experiment tracking
  • Open Source and Cloud Vendor Neutral
  • Deep Learning Teams
View Price Page
MLflow
Custom

Features

  • MLflow Tracking
  • MLflow Tracking Server
  • Experiment Management
  • Logging Data with Runs
  • Delta Lake Integration
  • Artifact Store
View Price Page

SCREENSHOTS

Determined AI Screenshots
View 1 screenshot(s)
MLflow Screenshots
View 0 screenshot(s)

INTEGRATIONS

  • Slack
    NA

ALTERNATIVES

Top alternatives to Determined AI

Prompt ai
Prompt ai
Everbridge IT Alerting
Everbridge IT Alerting
MLflow
MLflow
IFTTT
IFTTT
Online Check Writer
Online Check Writer

Top alternatives to MLflow

Google Cloud AutoML
Google Cloud AutoML
DVC Studio
DVC Studio
Amazon SageMaker
Amazon SageMaker
IFTTT
IFTTT
Online Check Writer
Online Check Writer