neptune-client
Airflow
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neptune-client | Airflow | |
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24 | 169 | |
536 | 34,485 | |
7.8% | 2.3% | |
9.7 | 10.0 | |
4 days ago | 4 days 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 âcopy-pasteâ their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase, Posthog, Auth0).
So past year and a half, Iâve 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 youâd like to share good examples Iâd 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
Airflow
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Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions
Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.
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Navigating Week Two: Insights and Experiences from My Tublian Internship Journey
In week Two, I contributed to the Apache Airflow repository.
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Airflow VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Best ETL Tools And Why To Choose
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.
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Simplifying Data Transformation in Redshift: An Approach with DBT and Airflow
Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.
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Share Your favorite python related software!
AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!
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Ask HN: What is the correct way to deal with pipelines?
I agree there are many options in this space. Two others to consider:
- https://airflow.apache.org/
- https://github.com/spotify/luigi
There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directoryâŠ
- "VocĂȘ veio protestar para ter acesso ao cĂłdigo fonte da urnas. O que Ă© o cĂłdigo fonte?" "NĂŁo sei" đ€Ą
- CĂłmo construir tu propia data platform. From zero to hero.
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Is it impossible to contribute to open source as a data engineer?
You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
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.
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
dagster - An orchestration platform for the development, production, and observation of data assets.
Caffe - Caffe: a fast open framework for deep learning.
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Porcupine  - On-device wake word detection powered by deep learning
Dask - Parallel computing with task scheduling