This book will help demonstrate how to use Kubeflow components, deploy them in GCP, and serve them in production using real-time data prediction. With Kubeflow KFserving, we’ll look at serving techniques, build a computer vision-based user interface in streamlit, and then deploy it to the Google cloud platforms, Kubernetes and Heroku. Next, we also explore how to build Explainable AI for determining fairness and biasness with a What-if tool. Backed with various use-cases, we will learn how to put machine learning into production, including training and serving.

After reading this book, you will be able to build your ML projects in the cloud using Kubeflow and the latest technology. In addition, you will gain a solid knowledge of DevOps and MLOps, which will open doors to various job roles in companies.

Get comfortable with the architecture and the orchestration of Kubernetes.
Learn to containerize and deploy from scratch using Docker and Google Cloud Platform.
Practice how to develop the Kubeflow Orchestrator pipeline for a TensorFlow model.
Create AWS SageMaker pipelines, right from training to deployment in production.
Build the TensorFlow Extended (TFX) pipeline for an NLP application using Tensorboard and TFMA.