MLflow/Kubeflow integration

Incorporating MLflow and Kubeflow as optional modules in Ilum will allow machine learning model tracking, training, and deployment seamlessly. This would enable the data engineers and the ML teams to manage their entire MLOps lifecycle from within Ilum, without using any other tools. https://mlflow.org/ - End-to-end machine learning workflows will allow you to train, track, and deploy models within Ilum using MLflow for experiment tracking and Kubeflow for scalable training and deployment. - You will now be able to use spark for large scale data processing and push your models directly to MLflow or Kubeflow for inference. - Maintain a record of various ML model versions to ensure reproducibility and governance. - You can use Ilum’s built-in security features to control access to your models and workflows. - You can use Kubeflow on Kubernetes for distributed training, which uses the already available Ilum infrastructure and offers a lot of flexibility. This will bring Ilum closer to an AI-ready platform for data teams to process, train, and deploy models in one place. Would love to hear your feedback on other features that can make it more useful!