You can freely replace SageMaker services with other components as your project grows and potentially outgrows SageMaker.

Airflow has been one of the most popular orchestrating tools for several years.

Mlflow supports both Conda and Docker-based projects.

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.

MLflow 2.0 also adds AutoML to MLflow Recipes, dramatically reducing the amount of time required to produce a high-quality model.

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.

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

ingest -> split -> transform -> train -> evaluate -> register

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.

Bon Appétit?

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.