Resources

How to Track XGBoost Experiments and Data Versions

1 min

Parth Tiwary

14th December, 2022

Video Tabular data Tracking Versioning

What will you learn?

How you can log XGBoost experiments and track multiple data versions locally or in the cloud using neptune.ai.

00:11
Creating a Neptune run
01:47
Tracking and versioning datasets stored in the S3
02:35
How does versioning work in Neptune?
03:06
Will Neptune store a dataset?
03:15
How can I log in a synchronous way?
03:43
How can I log and visualize samples of a dataset?
05:02
Is the data sample uploaded to the Neptune servers?
05:24
What are the namespaces?
05:42
How to use Neptune callback to track the XGBboost model training?
06:30
What are the benefits of using a Neptune integration?
07:07
Can I fetch metadata I logged to a Neptune run?
08:14
How to log test metrics?
09:00
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

See the docs on versioning datasets in runs.

Related resources

Role-Based Access Control: How Does it Work?

Text Classification Dashboard

How to Create and Save Custom Table Views

Model Training: MosaicML Composer + neptune.ai Integration [Example]

Explore more resources:

Content type

Case study Example dashboard Example project Product update Video Webinar

Area of interest

Computer vision NLP Reinforcement learning Tabular data Time series

Use case

Collaboration Comparison Debugging Evaluation Management Monitoring Reporting Reproducibility Tracking Versioning

Couldn’t find the use case you were looking for?

Just get in touch, and our ML team will create a custom demo for you.