Migrate from foundry_ml [planned deprecation] to palantir_models
The foundry_ml library and dataset-backed models have entered the planned deprecation phase of development and will be unavailable for use alongside the Python 3.9 deprecation in October 2025. Support remains available until the deprecation date. At this time, you should migrate your workflows to use the palantir_models library, either by training a new model or by wrapping the existing model in a model adapter. Additionally, models built with foundry_ml in Code Workbooks need to be rebuilt in Jupyter® Code Workspaces or Code Repositories. For guidance on building a new model with palantir_models, review how to train a model in Code Repositories or how to train a model in Jupyter® notebooks. Contact Palantir Support if you require additional help migrating your workflows.
As palantir_models does not support Spark ML models, you will need to migrate the models to scikit-learn or a similar single-node framework. Separately, we recommend replacing MetricSets with experiments where possible. Note that experiments now support image metrics, but do not yet support chart metrics.
Upgrade campaigns
Model migration campaign
Campaign details
A first campaign, out of two campaigns, will be published in Upgrade Assistant to help users migrate away from dataset-backed models. Only models that are in use will be flagged for review, while others will be marked as Ignored and filtered out from the campaign view by default.
Users will be able to designate a model asset replacement for a dataset-backed model directly from the dataset-backed model page. This information will be used by Upgrade Assistant to determine the migration status of the resource. Models with an identified replacement will have a status of Completed and will be filtered out from the campaign view by default. More generally, using this feature is recommended to direct consumers of the model to its replacement in the new framework.
To learn more:
- Review this example to learn how to migrate between the two frameworks;
- Review the migration FAQ for more details on how the migration works.
AIP-powered code suggestions
In environments where AIP is enabled, users will benefit from code migration suggestions powered by a Large Language Model (LLM) through AIP. These suggestions can be viewed by selecting the purple icon, as depicted below. The generated code can then be copied using the clipboard icon above each file.
While the LLM is able to help users to get started with migration, you will likely need to modify the code you are provided by the LLM in order to pass checks and produce a working model. Make sure to thoroughly review the code and the model outputs.
Consuming resources migration campaign
A second campaign will be published no later than April 2025 to surface resources which consume deprecated dataset-backed models. These resources include:
- Transforms jobs using dataset-backed models as inputs;
- Code workbooks or transform jobs producing dataset-backed models and other outputs;
- Modeling objectives with a live or batch deployment based on a dataset-backed model.