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!
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