Keras to Kubernetes: The Journey of a Machine Learning Model to Production

The article discusses how to use Keras and Kubernetes to deploy machine learning models. It covers practical steps such as training and packaging models, deploying them on a Kubernetes cluster, and managing data collection and processing. The article emphasizes the importance of understanding the entire ML model development lifecycle and deploying models to production.

Format: Paperback / softback
Length: 320 pages
Publication date: 07 June 2019
Publisher: John Wiley & Sons Inc

The use of artificial intelligence (AI) has experienced an exponential surge in recent years, transforming various industries and becoming a powerful force in the modern world. Within AI, machine learning (ML) and deep learning (DL) applications have emerged as key areas of focus, driven by their ability to learn from data and extract valuable insights.

Keras to Kubernetes: The Journey of a Machine Learning Model to Production is a comprehensive guide that takes you through the process of building and deploying DL models in Keras for real-world applications. Whether you're interested in recognizing product logos in images or extracting sentiment from text, this book provides hands-on learning examples and practical insights to help you get started.

In this book, you will learn how to use Keras, a popular deep learning framework, to build and train ML models. You'll explore the various layers of a deep learning model and understand how to optimize them for different tasks. You'll also learn about the importance of data preprocessing and feature engineering, which are crucial steps in building robust and accurate ML models.

Once you have trained your model, you'll learn how to package it as a web application container using Docker and Kubernetes. This allows you to deploy your model at scale on a Kubernetes cluster, making it accessible to a wide range of users and applications. You'll also explore the different deployment strategies available, such as batch deployment, real-time deployment, and serverless deployment, and learn how to choose the best approach for your specific use case.

In addition to deploying your model, you'll also learn about the various tools and libraries available for monitoring and managing your ML models in production. You'll explore the use of TensorFlow, a popular monitoring and visualization tool for deep learning models, and learn how to use it to track the performance of your models over time. You'll also learn about the use of Prometheus, a time-series database and monitoring system, and how to use it to collect and analyze metric data from your ML models.

Throughout the book, you'll find hands-on learning examples that demonstrate the practical steps involved in real-world ML implementations. These examples are designed to be reusable, allowing you to adapt them to your own projects and use cases. You'll also gain an understanding of the ML model development lifecycle, from ideation to deployment, and learn about the challenges and best practices involved in building production applications with deep learning.

Whether you're a data scientist, machine learning engineer, or software developer, Keras to Kubernetes: The Journey of a Machine Learning Model to Production is a valuable resource that will help you take your ML skills to the next level. With its comprehensive coverage of Keras, Kubernetes, and deep learning, this book provides you with the knowledge and tools you need to build and deploy successful ML models in the real world. So why wait? Start your journey to building production applications with deep learning today!

Weight: 440g
Dimension: 188 x 234 x 16 (mm)
ISBN-13: 9781119564836