Track and version your notebooks Log all your notebooks directly from Jupyter or Jupyter Lab. All you need is to install a Jupyter extension.
Manage your experimentation process Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking: - Metrics - Hyperparameters - Data versions - Model files - Images - Source code
Integrate with your workflow easily Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.
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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
Based on our record, neptune.ai seems to be more popular. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on Reddit, HackerNews and some other platforms. They can help you identify which product is more popular and what people think of it.
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 / about 2 months ago
There are a lot of other tools: neptune.ai, comet_ml, mlflow, etc. - Source: Reddit / 7 months ago
Optimizing the model with e.g., TensorBoard or NepTune. - Source: Reddit / 7 months ago
Neptune is a lightweight run management tool that helps you keep track of your machine learning runs. It offers 3 types of subscriptions, 2 of which are paid. If you are going to use this service individually, then you will get free access. - Source: Reddit / 9 months ago
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