Software Alternatives, Accelerators & Startups

AI Cheatsheet VS Evidently AI

Compare AI Cheatsheet VS Evidently AI and see what are their differences

AI Cheatsheet logo AI Cheatsheet

A tool to help you ace AI basics

Evidently AI logo Evidently AI

Open-source monitoring for machine learning models
  • AI Cheatsheet Landing page
    Landing page //
    2019-01-20
  • Evidently AI Landing page
    Landing page //
    2023-08-19

AI Cheatsheet videos

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Evidently AI videos

How to Monitor Machine Learning Models (Evidently AI)

Category Popularity

0-100% (relative to AI Cheatsheet and Evidently AI)
Developer Tools
38 38%
62% 62
AI
38 38%
62% 62
Android
100 100%
0% 0
Data Science And Machine Learning

User comments

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Social recommendations and mentions

Based on our record, Evidently AI seems to be more popular. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

AI Cheatsheet mentions (0)

We have not tracked any mentions of AI Cheatsheet yet. Tracking of AI Cheatsheet recommendations started around Mar 2021.

Evidently AI mentions (2)

  • [D] Using MLFlow for model performance tracking
    It is doable. However the main focus of MLFlow is in experiment tracking. I would suggest for you to look into another monitoring tools such evidentlyai . You can track more things than performance (e.g.data drift). Which may be helpful in a production setting. Source: almost 2 years ago
  • Five Data Quality Tools You Should Know
    Evidently is an open-source Python library that analyzes and monitors machine learning models. It generates interactive reports based on Panda DataFrames and CSV files for troubleshooting models and checking data integrity. These reports show model health, data drift, target drift, data integrity, feature analysis, and performance by segment. - Source: dev.to / over 2 years ago

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iko.ai - Real-time collaborative notebooks on your own Kubernetes clusters to train, track, package, deploy, and monitor your machine learning models.