Software Alternatives & Reviews

Prodigy VS neptune.ai

Compare Prodigy VS neptune.ai and see what are their differences

Prodigy logo Prodigy

Radically efficient machine teaching

neptune.ai logo 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.
  • Prodigy Landing page
    Landing page //
    2023-10-22
  • neptune.ai Landing page
    Landing page //
    2023-08-24

Track and version your notebooks Log all your notebooks directly from Jupyter or Jupyter Lab. All you need is to install a Jupyter extension.

Manage your experimentation process Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking: - Metrics - Hyperparameters - Data versions - Model files - Images - Source code

Integrate with your workflow easily Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.

Prodigy videos

The Prodigy - Movie Review

More videos:

  • Review - Prodigy Math Game Review
  • Review - PRODIGY MATH for Homeschool?! Hmm...

neptune.ai videos

Machine Learning Experiment Management with Neptune.ai - How to start

Category Popularity

0-100% (relative to Prodigy and neptune.ai)
AI
100 100%
0% 0
Data Science And Machine Learning
Product Lifecycle Management (PLM)
Data Science Notebooks
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Prodigy and neptune.ai

Prodigy Reviews

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neptune.ai Reviews

  1. Easy to use, not overdone, good for model management and collab

    Only negative is I didn't see it integrated with Azure, does with Google, AWS and one more. Looks real nice, and pretty powerful and plenty useful features for a data science group

Social recommendations and mentions

Prodigy might be a bit more popular than neptune.ai. We know about 25 links to it since March 2021 and only 22 links to neptune.ai. 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.

Prodigy mentions (25)

  • Launch HN: Encord (YC W21) – Unit testing for computer vision models
    This is really cool. The annotation-to-testing-to-annotation-etc. Feedback loop makes a ton of sense, and I'd encourage others who may be confused on this post to look at the Automotus case study https://encord.com/customers/automotus-customer-story/ for the annotation side, but my understanding is the relationship between model outputs and annotation steering is out of scope for that project - do you know of... - Source: Hacker News / 3 months ago
  • Against LLM Maximalism
    Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post. The steps... - Source: Hacker News / 8 months ago
  • Remote Work 2.0: The Tools, Trends, and Challenges of the Post-Pandemic Work Era
    Prodigy AI - Offers software engineers career coaching, skill assessment, and job matching. Visit Prodigy AI. - Source: dev.to / 9 months ago
  • [D] A model to extract relevant information from a Sample Ballot.
    I essentially want to use a Combo of OCR + NER to attempt to identify this, but I'm not sure NER is well suited for this, as it is not natural language, so there is little context to go off of. I was thinking of perhaps using Prodigy, a data annotation tool, to annotate Candidate Names, Races, etc, and perhaps it will be able to learn off of image data alone wheat these fields tend to look like. Source: about 1 year ago
  • Sampling leaves from a tree
    I come from a similar application area, where I try to tag (annotation/label) a taxonomy of products iteratively. You are trying something slightly different, AFAIU, labeling a flat set of songs, each song with a set of tags from ontology (directed graph)From an application point of view, this is what taxonomists often do, when migrating products from one catalog to another: mapping one taxonomy to another. There... Source: over 1 year ago
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neptune.ai mentions (22)

  • A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
    Neptune.ai - Log, store, display, organize, compare, and query all your MLOps metadata. Free for individuals: 1 member, 100 GB of metadata storage, 200h of monitoring/month. - Source: dev.to / 3 months ago
  • Show HN: A gallery of dev tool marketing examples
    Hi I am Jakub. I run marketing at a dev tool startup https://neptune.ai/ and I share learnings on dev tool marketing on my blog https://www.developermarkepear.com/. Whenever I'd start a new marketing project I found myself going over a list of 20+ companies I knew could have done something well to “copy-paste” their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase,... - Source: Hacker News / 7 months ago
  • How to structure/manage a machine learning experiment? (medical imaging)
    There are a lot of tools out there for experiment tracking (eg neptune.ai), but I'm really not sure whether that sort of thing is over the top for what I need to do. Source: 8 months ago
  • How to grow a developer blog to 3M annual visitors? with Jakub Czakon (Neptune.ai)
    Welcome to another episode of The Developer-led Podcast, where we dive into the strategies modern companies use to build and grow their developer tools. In this exciting episode, we're joined by Jakub Czakon, the CMO at Neptune.ai, a startup that assists developers in efficiently managing their machine-learning model data. Jakub is renowned not only for his role at Neptune.ai but also for his developer marketing... - Source: dev.to / 9 months ago
  • [D] Is there any all in one deep learning platform or software
    Tbh I have done a pretty good search on this topic, I couldn't find any. I thought maybe community could help me find one, if people like you (who works at neptune.ai) have the same opinion then it is what it is :). Anyway thank you for the suggestions that you gave, probably gonna use that. Source: 10 months ago
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What are some alternatives?

When comparing Prodigy and neptune.ai, you can also consider the following products

Enovia - ENOVIA offers product lifecycle management (PLM) solutions fostering innovation and operational excellence across industries.

Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.

Omnify PLM - Omnify PLM is a business-ready product lifecycle management solution.

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.

Arena PLM - Arena offers PLM solutions for manufacturing teams to speed prototyping, reduce scrap, and streamline supply chain management.

Weights & Biases - Developer tools for deep learning research