15 Matching Annotations
  1. Mar 2023
    1. You can freely replace SageMaker services with other components as your project grows and potentially outgrows SageMaker.

  2. Nov 2022
    1. in MLflow 2.0, the mlflow.evaluate() API for model evaluation is now stable and production-ready. With just a single line of code, mlflow.evaluate() creates a comprehensive performance report for any ML model.
    2. MLflow 2.0 also adds AutoML to MLflow Recipes, dramatically reducing the amount of time required to produce a high-quality model.

      AutoML in MLflow 2.0

    3. In MLflow 2.0, MLflow Recipes is now a core platform component with several new features, including support for classification models, improved data profiling and hyperparameter tuning capabilities.

      MLflow Recipes in MLflow 2.0

    1. See an example for combining mlflow and DVC e.g. here: https://github.com/mbunse/mlcomops/tree/meetup_erlangen

      Real project combining MLflow & DVC.

    2. Infos in the comments about DVC MLOps & one suggesting ClearML.

  3. Sep 2022
    1. ingest -> split -> transform -> train -> evaluate -> register

      I don't think it allows you to add steps other than the ones defined here.

  4. May 2021
    1. In short, MLflow makes it far easier to promote models to API endpoints on various cloud vendors compared to Kubeflow, which can do this but only with more development effort.

      MLflow seems to be much easier

    2. Bon Appétit?

      Quick comparison of MLflow and Kubeflow (check below the annotation)

    3. MLflow is a single python package that covers some key steps in model management. Kubeflow is a combination of open-source libraries that depends on a Kubernetes cluster to provide a computing environment for ML model development and production tools.

      Brief comparison of MLflow and Kubeflow

  5. Feb 2020