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Tensorflow Research Cloud VS Scale Nucleus

Compare Tensorflow Research Cloud VS Scale Nucleus and see what are their differences

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Tensorflow Research Cloud logo Tensorflow Research Cloud

Accelerating open machine learning research with Cloud TPUs

Scale Nucleus logo Scale Nucleus

The mission control for your ML data
  • Tensorflow Research Cloud Landing page
    Landing page //
    2021-10-16
  • Scale Nucleus Landing page
    Landing page //
    2023-08-20

Tensorflow Research Cloud videos

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Free TPUs through Tensorflow Research Cloud

Scale Nucleus videos

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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 Tensorflow Research Cloud and Scale Nucleus)

Tensorflow Research Cloud

Scale Nucleus

AI
33 33%
AI
67% 67
Developer Tools
31 31%
Developer Tools
69% 69
APIs
39 39%
APIs
61% 61
Tech
38 38%
Tech
62% 62

User comments

Share your experience with using Tensorflow Research Cloud 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.

Tensorflow Research Cloud mentions (0)

We have not tracked any mentions of Tensorflow Research Cloud yet. Tracking of Tensorflow Research Cloud 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 Tensorflow Research Cloud and Scale Nucleus, you can also consider the following products

Google Cloud TPUs - Build and train machine learning models with Google

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

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

Sourceful - A search engine for publicly-sourced Google docs

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

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

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