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Spell VS Scale Nucleus

Compare Spell VS Scale Nucleus and see what are their differences

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Spell logo Spell

Deep Learning and AI accessible to everyone

Scale Nucleus logo Scale Nucleus

The mission control for your ML data
  • Spell Landing page
    Landing page //
    2022-09-23
  • Scale Nucleus Landing page
    Landing page //
    2023-08-20

Spell videos

+ Add

Love Spells 24 Reviews 💙 My experience with their spells (excited to share)

More videos:

  • Review - SPELL Opulent Decay Album Review | Overkill Reviews
  • Review - LETS REVIEW Spells That Work

Scale Nucleus videos

+ Add

Using Scale Nucleus & Rapid to Label New Datasets Efficiently

More videos:

  • Review - Scale Nucleus: Send to Annotation
  • Review - Scale Nucleus: Find Missing Annotations

Category Popularity

0-100% (relative to Spell and Scale Nucleus)

Spell

Scale Nucleus

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Developer Tools
52 52%
Developer Tools
48% 48
AI
66 66%
AI
34% 34
APIs
0 0%
APIs
100% 100

User comments

Share your experience with using Spell and Scale Nucleus. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Scale Nucleus 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.

Spell mentions (0)

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

Scale Nucleus mentions (2)

  • [Discussion] The most painful thing about machine learning
    At Scale we built a tool for model debugging in computer vision called Nucleus (scale.com/nucleus) designed exactly for this, which is free try out if you're curious to see where your model predictions are most at odds with your ground truth. Source: over 2 years ago
  • Unit Testing for Production ML Workflows?
    To address your point about gathering edge cases, which can also be defined as cases of low model fidelity for our use cases, there is active learning and tools such as Aquarium Learning and Scale Nucleus which make it easy to implement into workflows. Source: almost 3 years ago

What are some alternatives?

When comparing Spell and Scale Nucleus, you can also consider the following products

neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

Aquarium - Improve ML models by improving datasets they’re trained on

Neuton.AI - No-code artificial intelligence for all

PerceptiLabs - A tool to build your machine learning model at warp speed.

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

ML Image Classifier - Quickly train custom machine learning models in your browser

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