neptune-client
unionml
neptune-client | unionml | |
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24 | 6 | |
536 | 330 | |
5.6% | 1.2% | |
9.7 | 4.0 | |
7 days ago | 6 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
neptune-client
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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, Posthog, Auth0).
So past year and a half, I’ve been screenshoting examples of how companies that are good at dev marketing do things like pricing, landing page design, ads, videos, blog conversion ideas. And for each example I added a note as to why I thought it was good.
Now, it is ~140 examples organized by tags so you can browse all or get stuff for a particular topic.
Hope it is helpful to some dev tool founders and marketers in here.
wdyt?
Also, I am always looking for new companies/marketing ideas to add to this, so if you’d like to share good examples I’d really appreciate it.
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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.
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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 endeavors, including the influential newsletter Developer Markepear and a thriving developer marketing Slack community.
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[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.
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New Data Scientist, want to get into MLOps, where to start?
To get started with MLOps, you will need to have some foundational skills in Python, SQL, mathematics, and machine learning algorithms and libraries. You will also need to learn about databases, model deployment, continuous integration, continuous delivery, continuous monitoring, and other best practices of MLOps. You can find some useful resources for each of these topics in the following blogs on neptune.ai (disclosure: I work for Neptune):
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Does a fully sentient (Or at least as sentient as you and me) AI with free will have a soul?
arxiv.org2. apro-software.com3. en.wikipedia.org4. neptune.ai
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[D] The hype around Mojo lang
Other companies followed the same route to promote their paid product, e.g. plotly -> dash, Pytorch Lightning -> Lightning AI, run.ai, neptune.ai . It's actually a fair strategy, but some people may fear the conflict of interest. Especially, when the tools require some time investment, and it seems like a serious vendor lock-in. Investing some time to learn a tool is not such a big deal, but once you adapt a workflow of an entire team it can be tough to go back.
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[P] New Open Source Framework and No-Code GUI for Fine-Tuning LLMs: H2O LLM Studio
track and compare your model performance visually. In addition, Neptune integration can be used.
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[D] New features and current problems with ml infrastructure?
I am working on a startup, I was wondering what people think are some gaps in current machine learning infrastructure solutions like WandB, or Neptune.ai.
- All your ML model metadata in a single place
unionml
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Who needs MLflow when you have SQLite?
Checkout Flyte.org and it’s sibling project https://www.union.ai/unionml
- UnionML: the easiest way to build and deploy machine learning microservices
- GitHub - unionai-oss/unionml: UnionML: the easiest way to build and deploy machine learning microservices
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Show HN: UnionML – a Python framework for building ML microservices
Hi HN!
Niels here. I'm the creator of *UnionML*, a Python MLOps framework that removes the boilerplate and friction associated with building and deploying machine learning systems to production.
I've been training and deploying models for almost a decade now, and one pain-point I've consistently had is managing the complexity of building and maintaining an ML stack that works for the entire model development lifecycle - from prototyping to production.
UnionML is built on top of Flyte (https://www.flyte.org) and exposes a functional interface for defining the building blocks of your ML application via decorators -- think Flask or FastAPI method endpoints -- and UnionML takes care of bundling them into microservices for different use cases such as:
- model training
- batch prediction
- online prediction
- (more coming soon!)
This project aims to unify the rich ecosystem of data, ML, and MLOps tools that have emerged over the last decade or so (e.g. MLFlow, Sagemaker, Spark, etc.) to provide a nice UX for model developers, in both individual and team settings.
It's very early days for this project, so if you're interested in getting involved or learning more, you can go to the:
- Docs: https://unionml.readthedocs.io/en/latest/
- Repo: https://github.com/unionai-oss/unionml
- Slack: https://flyte-org.slack.com/archives/C03JL38L65V
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
ploomber-engine - A toolbox 🧰 for Jupyter notebooks 📙: testing, experiment tracking, debugging, profiling, and more!
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
rubicon-ml - Capture all information throughout your model's development in a reproducible way and tie results directly to the model code!
Caffe - Caffe: a fast open framework for deep learning.
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Porcupine - On-device wake word detection powered by deep learning
Theano - Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor
Caffe2
lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡