Software Alternatives & Reviews

neptune.ai Reviews

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.

Latest User Reviews

  1. User avatar
     
    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

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Social recommendations and mentions

We have tracked the following product recommendations or mentions on Reddit and HackerNews. They can help you see what people think about neptune.ai and what they use it for.
  • 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 / 9 days 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: Reddit / 10 days 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 / 26 days 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: Reddit / about 1 month 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: Reddit / 6 months 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: Reddit / 9 months 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: Reddit / 9 months 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 / 12 months ago
  • [D] Alternatives to W&B?
    There are a lot of other tools: neptune.ai, comet_ml, mlflow, etc. - Source: Reddit / over 1 year ago
  • Multivariant Time Series Forecasting with LSTM - course advice
    Optimizing the model with e.g., TensorBoard or NepTune. - Source: Reddit / over 1 year ago

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