neptune.ai (@neptune

neptune.ai

12.9K posts

neptune.ai

@neptune_ai

Experiment tracker purpose-built for foundation model training. We tweet about #LLM best practices & other cool stuff. Read our blog at neptune.ai/blog

Warsaw, Poland

Joined January 2018

Pinned
neptune.ai
@neptune_ai
Jan 29, 2025
We built Neptune Scale to let you monitor such training and debug any issues quickly. Now available in beta: buff.ly/3Yihk7P Coming soon for everyone.
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neptune.ai
@neptune_ai
Feb 27, 2020
We have a treat for all @PyTorch users out there! "8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem" First-hand info about: - Philosophy - Build-in features - Extension capabilities - and way more bit.ly/32uKJx7
neptune.ai
@neptune_ai
Apr 20, 2019
Just updated the @fastdotai integration with @NeptuneML so that you can have your experiments tracked, hosted and ready to share with others. Check out the blog post about it bit.ly/2vcJJxI
neptune.ai
@neptune_ai
Jun 27, 2019
Track metrics/hyperparameters/code of @fastdotai experiments All it takes is one callback. This @Medium post explains how bit.ly/2vcJJxI #DataScience #MachineLearning
neptune.ai
@neptune_ai
Jun 4, 2021
Want to understand with code how to build #BERT? Check this article! @nielspace07 uses @pytorch and breaks the process into 4 sections: - Preprocessing - Building model - Loss and Optimization - Training bit.ly/3fCuG8u
neptune.ai
@neptune_ai
Jun 7, 2021
If you want to know what are the components of #BERT, and dive into the code - check this article. bit.ly/3fCuG8u
neptune.ai
@neptune_ai
Aug 17, 2022
There are 2 types of #ML engineers: Task ML engineer and Platform ML engineer @sh_reya explains in “Thoughts on #MLEngineering After a Year of my PhD”
neptune.ai
@neptune_ai
Jan 27, 2021
Good news! We integrated Neptune with another awesome library - Keras Tuner. You can now: -see charts of logged metrics -see the parameters tried -log hyperparameter search space And more! Docs 👉 bit.ly/2Yfp9eC Thanks @fchollet and the team for this great library 🙏
neptune.ai
@neptune_ai
Nov 4, 2019
#ToolAlert dabl: an awesome library by @amuellerml that reduces boilerplate when creating baseline ML solutions. With (dabl.) clean, plot, AnyClassifier, and explain you can do pretty much everything you need with a one-liner. Check it out: amueller.github.io/dabl/dev/quick…
neptune.ai
@neptune_ai
Nov 5, 2020
If you're training deep learning models, @fastdotai should definitely be your tool. And if you want to additionally monitor your training (believe us, you should want that), try Neptune-fastai integration. It's just one additional callback. Docs: bit.ly/38iUByG
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neptune.ai
@neptune_ai
Jun 6, 2021
Before creating a model, it’s a good idea to ask ourselves: “Can this problem be solved without #ML?” Yes? Then head on over to this article 👇 bit.ly/3fokz72
neptune.ai
@neptune_ai
Sep 20, 2022
Dear practical, pragmatic #ML folks out there. If there is one conference you should attend this year, it is probably @normconf 2022. Here is why:
neptune.ai
@neptune_ai
Aug 12, 2022
#MLOps stack at companies doing #ML at a reasonable scale. How do they choose their tools? Veeeeery pragmatically. First thing is to understand what you actually need. Which part of the stack you need to do well and which part not so much.
neptune.ai
@neptune_ai
Aug 26, 2022
The secret key is the interoperability of the components. That is the difference between success and frustration when building an #MLOps platform. In the classic debate between build, in-house vs buy best-of-breed, the answer (when you cut the fluff out) is almost always both.