What will you learn?
How you can log XGBoost experiments and track multiple data versions locally or in the cloud using neptune.ai.
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Creating a Neptune run
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Tracking and versioning datasets stored in the S3
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How does versioning work in Neptune?
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Will Neptune store a dataset?
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How can I log in a synchronous way?
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How can I log and visualize samples of a dataset?
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Is the data sample uploaded to the Neptune servers?
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What are the namespaces?
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How to use Neptune callback to track the XGBboost model training?
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What are the benefits of using a Neptune integration?
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Can I fetch metadata I logged to a Neptune run?
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How to log test metrics?
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Does Neptune store my trained models?
Important: This video was created in December 2022. For the most up-to-date code examples, please refer to the Neptune docs.
Other useful resources
More about How to Track XGBoost Experiments and Data Versions
See also
Train, Track, and Deploy Your Models: Neptune + Modelbit Integration
See also
.upload(“product_updates_september_2023”)
See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
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