How to Log Different Phases of the MLOps Lifecycle Using the XGBoost Integration
What will you learn?
How you can log different phases of the MLOps lifecycle to Neptune using the XGBoost integration.
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Introduction to the use case
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How to initialize a run using the XGBoost integration?
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How to version datasets in Neptune?
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How to log a pandas dataframe to Neptune?
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How to download a model file from Neptune?
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How to query and filter runs?
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UI walkthrough
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How to download and fine-tune an existing model?
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UI walkthrough of the fine-tuning namespace
Important: This video was created in May 2021. For the most up-to-date code examples, please refer to the Neptune-XGBoost integration docs.
neptune.ai is an MLOps stack component for experiment tracking. So we’re constantly working on making it easy to integrate with other parts of the workflow.
It is already integrated with 25+ tools and libraries, and the list is growing. You can check our roadmap to see what’s currently under development.
Other useful resources
Check the docs on what you can log and display in Neptune.
Learn more about the XGBoost integration.
More about How to Log Different Phases of the MLOps Lifecycle Using the XGBoost Integration
See also
What is a Project in Neptune?
See also
Model Training: Detectron2 + neptune.ai Integration [Example]
See also
Model Training: Prophet + neptune.ai Integration [Example]
See also
Create AzureML Pipeline – Workshop with Aurimas Griciūnas
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