For AI agents: the complete documentation index is available at https://docs.dataplatform.ovh.net/llms.txt, the full documentation bundle is available at https://docs.dataplatform.ovh.net/llms-full.txt, and this page is available as Markdown at https://docs.dataplatform.ovh.net/legacy/ml-pipelines-configure.md.
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    • Configure a pipeline

    Warning

    This page describes the Machine Learning Manager, a service of the ForePaaS Legacy Platform that is not available on OVHcloud Data Platform. See the current documentation.

    Each pipeline can be configured from the data input to the deployment options in order to create accurate production-grade prediction models.

    machinelearning

    The five configuration steps

    There are five core steps to setting up a pipeline on ForePaaS.

    1
    Dataset

    Dataset preparation

    Specify everything related to the train and test datasets of your model.

    2
    Training

    Training procedure

    Set up the estimator at the basis of your model as well as the scoring and validation options needed to fine-tune it.

    3
    Tuning

    Hyper-parameter tuning

    Fine-tune the hyper-parameters of your estimator using our intuitive and insightful studio.

    4
    Validation

    Model selection

    Compare models that have been trained since the creation of your pipeline and decide which to deploy.

    5
    Deployment

    Deployment settings

    Specify the various settings for your production-grade inference service

    Manage execution options

    Once a pipeline is set up, you can manage various execution options to ensure a reliable life-cycle of your models in a production environment. Learn more about the execution of a pipeline and the life-cycle of a model below:

    Manage a pipeline's execution

    Go further

    If you need training or technical assistance to implement our solutions, contact your sales representative or click on this link to get a quote and ask our Professional Services experts for a custom analysis of your project.

    Ask questions, give your feedback and interact directly with the team building the Data Platform on the dedicated Discord channel.

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    Join our community of users.