http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
the journey of a machine learning model to production
1 online resource (323 pages : : illustrations.)
Artificial intelligence (AI) has, in one form or another, been in existence for over six decades. However, recent years have seen an enormous increase in the amount of collectable data and major advancements in algorithms and computer hardware. Within the realm of AI technology, machine learning (ML) and deep learning (DL) applications in particular have undergone significant growth Keras, one of the most popular DL frameworks, can quickly describe a DL model, begin training it on data, and generate more data by modifying existing data. Kubernetes is an application engine that manages applications packaged as containers, handling all the infrastructure constraints such as scaling, fail-over, and load balancing. With the power, flexibility, and virtually limitless applications of Keras and Kubernetes comes a caveat-- they can be challenging to develop and deploy effectively without proper guidance. "Keros to Kubernetes: the journey of a machine learning model to production" offers step-by-step instructions on how to build a Keras model to scale and deploy on a Kubernetes cluster. This timely and accessible guide takes readers through the entire model-to-production process, covering topics such as model serving, scaling, load balancing, API development, algorithm-as-a-service (AaaS), and more. Real-world examples help readers build a Keras model for detecting logos in images, package it was a web application container, and deploy it at scale on a Kubernetes cluster
Big data and artificial intelligence -- Machine learning -- Handling unstructured data -- Deep learning using Keras -- Advanced deep learning -- Cutting-edge deep learning projects -- AI in the modern software world -- Deploying AI models as microservices -- Machine learning development lifecycle -- A platform for machine learning.
Includes bibliographical references and index.