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Affise VS Monitor ML

Compare Affise VS Monitor ML and see what are their differences

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Contents:
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  4. » Alternatives

Affise logo Affise

Affise offers performance marketing solution to manage and analyze affiliates, advertisers, creatives, conversions, offers, and payments.

Monitor ML logo Monitor ML

Real-time production monitoring of ML models, made simple.
  • Affise Landing page
    Landing page //
    2023-09-28
  • Monitor ML Landing page
    Landing page //
    2021-10-12

Affise

Website
affise.com
Pricing URL
Official Affise Pricing
Release Date
2016 January
Startup details
Country
Lithuania
State
Vilniaus Apskritis
City
Vilnius
Founder(s)
Dmitri Zotov
Employees
100 - 249
Edit details

Monitor ML

Website
monitorml.com
Pricing URL
-
Release Date
-
Edit details

Affise videos

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Affise, tracking platform for more than 1000 affiliate networks

More videos:

  • Review - Affise - SaaS Marketing Platform, where you can grow your affiliate business

Monitor ML videos

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Category Popularity

0-100% (relative to Affise and Monitor ML)

Affise

Monitor ML

Advertising
100 100%
Advertising
0% 0
Developer Tools
0 0%
Developer Tools
100% 100
Video Ads
100 100%
Video Ads
0% 0
AI
0 0%
AI
100% 100

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What are some alternatives?

When comparing Affise and Monitor ML, you can also consider the following products

Optmyzr - Optmyzr AdWords Tools. Optimization Solutions, Quality Score Tracker, Landing Page Checker, and more. Free Trial Available.

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

LiveIntent - LiveIntent is a web platform that offers effective e-mail advertising services for marketers and publishers.

Qualdo™ - Monitor mission-critical data quality & ML issues and drifts

Adobe Primetime - Adobe Primetime is a multiscreen TV platform that helps broadcasters create and monetize viewing experiences.

TensorFlow Lite - Low-latency inference of on-device ML models

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