What will you learn?
How you can log XGBoost experiments and track multiple data versions locally or in the cloud using neptune.ai.
-
Creating a Neptune run
-
Tracking and versioning datasets stored in the S3
-
How does versioning work in Neptune?
-
Will Neptune store a dataset?
-
How can I log in a synchronous way?
-
How can I log and visualize samples of a dataset?
-
Is the data sample uploaded to the Neptune servers?
-
What are the namespaces?
-
How to use Neptune callback to track the XGBboost model training?
-
What are the benefits of using a Neptune integration?
-
Can I fetch metadata I logged to a Neptune run?
-
How to log test metrics?
-
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
Train, Track, and Deploy Your Models: Neptune + Modelbit Integration
.upload(“product_updates_september_2023”)
What is a Project in Neptune?
Model Training: Detectron2 + neptune.ai Integration [Example]
Explore more resources:
Content type
Area of interest
Area of interest
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.