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Scale Nucleus VS Managed MLflow

Compare Scale Nucleus VS Managed MLflow and see what are their differences

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Scale Nucleus logo Scale Nucleus

The mission control for your ML data

Managed MLflow logo Managed MLflow

Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.
  • Scale Nucleus Landing page
    Landing page //
    2023-08-20
  • Managed MLflow Landing page
    Landing page //
    2023-05-15

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

Managed MLflow videos

No Managed MLflow videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Scale Nucleus and Managed MLflow)

Scale Nucleus

Managed MLflow

Developer Tools
100 100%
Developer Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
AI
100 100%
AI
0% 0
Data Science Notebooks
0 0%
Data Science Notebooks
100% 100

User comments

Share your experience with using Scale Nucleus and Managed MLflow. 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.

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

Managed MLflow mentions (0)

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

What are some alternatives?

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

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

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.

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

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.

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

Weights & Biases - Developer tools for deep learning research

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