neptune.ai Reviews and details

Social recommendations and mentions

• 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 / 8 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

• 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... Source: 10 months ago

• [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... Source: 12 months ago

• [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. Source: about 1 year ago

• [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. Source: about 1 year ago

• Automated “Internal” Web Application Penetration Testing

  That's something that devops are working but they sre trying this neptune.ai. Source: about 1 year ago

• Any MLOps platform you use?

  Neptune.ai, which promises to streamline your workflows and make collaboration a breeze. Source: about 1 year ago

• A huge list of AI/ML news sources

  Blog – neptune.ai - Metadata store for MLOps, built for teams that run a lot of experiments. (RSS feed: https://neptune.ai/blog/feed). - Source: dev.to / over 1 year ago

• Opinions about W&B/MLFlow

  Helpful. Thanks a ton. Please, could you change it from "neptune.ml" to "neptune.ai" when you get the chance? Appreciate it. Source: over 1 year ago

• free-for.dev

  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 / over 1 year ago

• Machine Learning experiment tracking library for Rust

  Therefore I am looking for frameworks which can help me with tracking all the ML experiments. There are an endless plethora of such libraries for Python, most notably perhaps [wandb](wandb.ai), but others include Neptune, Comet ML and TensorBoard. Source: over 1 year ago

• [D] Maintaining documentation with live results from experiments

  In the case of neptune.ai we don't have this feature but you can query and retrieve the metadata you logged programmatically using the Python Client and use it to create a custom report/dashboard using tools like notion, streamlit, gradio, dash and etc. You also can have a cron-job that updates the report periodically or when there is a new experiment logged to Neptune. Source: almost 2 years ago

• What are the differences between MLflow and neptune?

  Hello u/MLBoi_TM! I was wondering: The pros/cons you've listed, is this comparing Managed MLflow <> neptune.ai or the OSS MLflow compenent <> neptune.ai? Source: about 2 years ago

• What are the differences between MLflow and neptune?

  The key difference between MLflow and neptune.ai on a shallow level is really that neptune.ai does not offer a standalone OSS solution. Apart from that, its offering overlaps with MLflow's in the sense that it focuses on experiment tracking (incl. Metadata store) as well as model artifact management ("model registry"). Of course, there' lots of differences in the detail then. However, since I've never used... Source: about 2 years ago

• Taking on the ML pipeline challenge: why data scientists need to own their ML workflows in production

  So, if you even want to use MLFlow to track your experiments, run the pipeline on Airflow, and then deploy a model to a Neptune Model Registry, ZenML will facilitate this MLOps Stack for you. This decision can be made jointly by the data scientists and engineers. As ZenML is a framework, custom pieces of the puzzle can also be added here to accommodate legacy infrastructure. - Source: dev.to / over 2 years ago

• [D] Alternatives to W&B?

  There are a lot of other tools: neptune.ai, comet_ml, mlflow, etc. Source: almost 3 years ago

• Multivariant Time Series Forecasting with LSTM - course advice

  Optimizing the model with e.g., TensorBoard or NepTune. Source: almost 3 years ago