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Label Studio VS Evidently AI

Compare Label Studio VS Evidently AI and see what are their differences

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Label Studio logo Label Studio

Open Source Data Labeling Platform for AI Model Tuning

Evidently AI logo Evidently AI

Open-source monitoring for machine learning models
  • Label Studio Landing page
    Landing page //
    2023-10-15
  • Evidently AI Landing page
    Landing page //
    2023-08-19

Label Studio videos

+ Add

Installing Label Studio Plus Overview of Basic Features

More videos:

  • Review - White Label Studio Review & Coupon
  • Review - Label Studio: Natural Language Annotation & Cloud Storage Integration

Evidently AI videos

+ Add

How to Monitor Machine Learning Models (Evidently AI)

Category Popularity

0-100% (relative to Label Studio and Evidently AI)

Label Studio

Evidently AI

AI
21 21%
AI
79% 79
Developer Tools
11 11%
Developer Tools
89% 89
Data Labeling
100 100%
Data Labeling
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100

User comments

Share your experience with using Label Studio and Evidently AI. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Evidently AI should be more popular than Label Studio. 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.

Label Studio mentions (1)

  • Annotation is dead
    If instead you have a cohort on hand — -i.e., you do not want to send your data to a third party for any reason, or perhaps you have energetic undergrads — -then you could alternatively consider local, open-source annotation such as CVAT and Label Studio. Finally, nowadays, you might instead work with Large Multimodal Models to have them annotate your data; more on this awkward angle later. - Source: dev.to / about 2 months ago

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

What are some alternatives?

When comparing Label Studio and Evidently AI, you can also consider the following products

KopiKat - Generative image data augmentation tool preserving annotations. Enhance the precision of AI models without modifying the network structure.

ML Showcase - A curated collection of machine learning projects

Theneo - Build Stripe-like API docs in just a few clicks

Censius.ai - Building the future of MLOps

Analyzr - The simple, no-code solution for predictive analytics

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

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