Open-Source ML Platforms

In the previous chapter, we covered how Kubernetes can be used as the foundational infrastructure for running ML tasks, such as running model training jobs or building data science environments such as Jupyter Notebook servers. However, to perform these tasks at scale and more efficiently for large organizations, you will need to build ML platforms with the capabilities to support the full data science lifecycle. These capabilities include scalable data science environments, model training services, model registries, and model deployment capabilities.

In this chapter, we will discuss the core components of an ML platform and explore additional open-source technologies that can be used for building ML platforms. We will begin with technologies designed for building a data science environment capable of supporting a large number of users for experimentation. Subsequently, we will delve into various technologies for model training, model registries, model...