Summary

In this chapter, you have gained an understanding of the core architecture components of a typical ML platform and their capabilities. We have explored various open-source technologies such as Kubeflow, MLflow, TensorFlow Serving, Seldon Core, Triton Inference Server, Apache Airflow, and Kubeflow Pipelines. Additionally, we have discussed different strategies for approaching the design of an ML platform using open-source frameworks and tools.

While these open-source technologies offer powerful features for building sophisticated ML platforms, it is important to acknowledge that constructing and maintaining such environments requires substantial engineering effort and expertise, especially when dealing with large-scale ML platforms.

In the next chapter, we will delve into fully managed, purpose-built ML solutions that are specifically designed to facilitate the development and operation of ML environments. These managed solutions aim to simplify the complexities of...