I like the way the book is presented and it does draw your interest and highlight the benefits of using KubeFlow through practical real world examples.
Kubeflow simplifies and automates machine learning tasks like interactive analysis, complex pipelines, and model training. Seamlessly push models to production in the containerized and distributed environment and scale your ML infrastructure from your laptop to a Kubernetes cluster.
In Kubeflow in Action
you will learn how to:
Kubeflow in Action
- Set up interactive data science notebooks in a modern containerized environment
- Create pipelines for complex data processing and model training jobs
- Serve models in production for applications
- Train models on large datasets using distributed computing resources
- Use distributed resources to optimize model hyperparameters and architectures
- Make production deployments
is an authoritative hands-on guide to deploying machine learning to production using the Kubeflow MLOps platform. Each stage of the ML workflow is explored and illustrated with engaging use cases that are based on tasks regularly tackled by data scientists. You’ll learn how Kubeflow can support training models, automatically tune hyperparameters, and speed up reinforcement learning. Each use case is explored from both a data science and software engineering perspective, so you benefit from a 360° understanding of Kubeflow.
about the technology
Kubeflow is a Kubernetes-native MLOps platform that easily installs on clusters and is instantly familiar to anyone who’s worked with Kubernetes. It’s perfect for handling large-scale infrastructure for AI workloads, with support for distributed machine learning training and complex data pipelines.
about the book
Kubeflow in Action
shows you how to utilize Kubeflow to rapidly scale machine learning projects from a laptop to a distributed cluster. You’ll kick off with a rapid introduction to containers, benefit from careful guidance on Kubeflow’s installation and initial setup, and master core Kubeflow tasks like storing data, training models, and monitoring metrics.
Detailed use cases help show how to construct complex pipelines, automate hyperparameter tuning, and implement network architecture search. You’ll quickly progress to a deep dive into Kubeflow’s more advanced uses, including training distributed models, deployment, A/B testing, and infrastructure monitoring to help trigger actions based on incoming data.
about the reader
For data scientists and data engineers. Data scientists will benefit from learning to handle data pipeline infrastructure issues as well as scaling techniques. Data engineers will learn to streamline their daily tasks and support their scientist colleagues.
about the author
is a principal software engineer at Red Hat, in the AI Services product team. She is also the technical and community lead for Open Data Hub, an open-source project providing an end-to-end AI/ML platform including Kubeflow on Openshift.
is a senior principal software engineer and data scientist in the AI Center of Excellence at Red Hat. In the past, Sanjay has worked at CERN, Goldman Sachs, and Palantir.