Description
| Product ID: | 9781119564836 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | US |
| Title: | Keras to Kubernetes |
| Subtitle: | The Journey of a Machine Learning Model to Production |
| Authors: | Author: Dattaraj Rao |
| Page Count: | 320 |
| Subjects: | Algorithms and data structures, Algorithms & data structures |
| Description: | Select Guide Rating Build a Keras model to scale and deploy on a Kubernetes cluster We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, we?re seeing a particular growth in Machine Learning (ML) and Deep Learning (DL) applications. ML is all about learning relationships from labeled (Supervised) or unlabeled data (Unsupervised). DL has many layers of learning and can extract patterns from unstructured data like images, video, audio, etc. Keras to Kubernetes: The Journey of a Machine Learning Model to Production takes you through real-world examples of building DL models in Keras for recognizing product logos in images and extracting sentiment from text. You will then take that trained model and package it as a web application container before learning how to deploy this model at scale on a Kubernetes cluster. You will understand the different practical steps involved in real-world ML implementations which go beyond the algorithms. ? Find hands-on learning examples ? Learn to uses Keras and Kubernetes to deploy Machine Learning models ? Discover new ways to collect and manage your image and text data with Machine Learning ? Reuse examples as-is to deploy your models ? Understand the ML model development lifecycle and deployment to production If you?re ready to learn about one of the most popular DL frameworks and build production applications with it, you?ve come to the right place! |
| Imprint Name: | John Wiley & Sons Inc |
| Publisher Name: | John Wiley & Sons Inc |
| Country of Publication: | GB |
| Publishing Date: | 2019-06-07 |
