Open source technologies for building ML platforms
While it is possible to run different ML tasks by creating and deploying different standalone ML containers in a Kubernetes cluster, this can become quite complex to manage when you have to do this at scale for a large number of users and ML workloads. This is where open source technologies such as Kubeflow, MLflow, Seldon Core, GitHub, and Airflow come in. Next, let's take a closer look at how these open source technologies can be used for building data science environments, model training services, model inference services, and ML workflow automation.
Using Kubeflow for data science environments
Kubeflow is a Kubernetes-based, open source ML platform that provides a number of ML-specific components. Kubeflow runs on top of Kubernetes and provides the following capabilities:
- A central UI dashboard
- A Jupyter notebook server for code authoring and model building
- A Kubeflow pipeline for ML pipeline orchestration...
