The Data Science Lifecycle provides a process of steps that you can use to structure your data science projects:

• Data Science Lifecycle presentation

The presentation outlines how you can use TensorFlow Extended (TFX) as an end-to-end platform for deploying production machine learning pipelines. A TFX pipeline is a sequence of components that implement an ML pipeline which is specifically designed for scalable, high-performance machine learning tasks. Components are built using TFX libraries which can also be used individually.

Resources

TensorFlow Extended Installation Tutorials:

• 💻 Local installation of TensorFlow & TensorFlow Extended

• 💻 Build your first TFX pipeline

How to use Kubeflow pipelines

• 💻 Local installation of Kubeflow (MiniKF)

• 💻 Build your first Kubeflow pipeline

TensorFlow Extended resources to learn more about the data science lifecycle steps:

• Text classification example

• Introduction to the TensorFlow ecosystem