Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker)
<a href="https://www.linkedin.com/in/dirk-jan-verdoorn/">Dirk-Jan Verdoorn</a> &#8211; Data Scientist at Dept Agency
<a href="https://www.kubeflow.org/">Kubeflow</a>. (From the website:) The Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow.
<a href="https://www.tensorflow.org/tfx">TensorFlow Extended (TFX)</a>. If using TensorFlow with Kubeflow, combine with TFX for maximum power. (From the website:) TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. When you&#8217;re ready to move your models from research to production, use TFX to create and manage a production pipeline.
Alternatives:
<ul>
<li><a href="https://airflow.apache.org/">Airflow</a></li>
<li><a href="https://mlflow.org/">MLflow</a></li>
</ul>

Tyler Renelle

MLA 020 Kubeflow

Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker) Dirk-Jan Verdoorn &#8211; Data Scientist at Dept Agency Kubeflow. (From the website:) The Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but [&hellip;]

Machine Learning Guide Episode #52

MLA 020 Kubeflow