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

Dattaraj Rao

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商品描述

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

商品描述(中文翻譯)

使用Keras建立一個模型,並在Kubernetes集群上進行擴展和部署

在過去幾年中,我們見證了人工智慧(AI)的指數級增長。AI正在成為新的電力,觸及從零售到製造到醫療保健到娛樂等各個行業。在AI中,我們尤其看到了機器學習(ML)和深度學習(DL)應用的快速增長。ML是關於從有標籤(監督)或無標籤(非監督)的數據中學習關係。DL具有多層學習,可以從圖像、視頻、音頻等非結構化數據中提取模式。

《從Keras到Kubernetes:機器學習模型到生產的旅程》通過實際案例介紹了在Keras中構建DL模型的過程,例如識別圖像中的產品標誌和從文本中提取情感。然後,您將把訓練好的模型打包成Web應用程序容器,並學習如何在Kubernetes集群上進行大規模部署。您將了解實際機器學習實施中涉及的不同實用步驟,這些步驟超越了算法本身。

- 尋找實踐學習的例子
- 學習使用Keras和Kubernetes部署機器學習模型
- 發現使用機器學習收集和管理圖像和文本數據的新方法
- 直接重用例子來部署您的模型
- 理解機器學習模型開發生命周期和部署到生產環境的過程

如果您準備好學習最受歡迎的DL框架之一並使用它來構建生產應用程序,那麼您來對地方了。

作者簡介

DATTARAJ JAGDISH RAO is a Principal Architect at GE Transportation (now a part of Wabtec Corporation). He has been with GE for 19 years working for Global Research, Energy and Transportation. Currently, he leads the Artificial Intelligence (AI) strategy for the global business, which involves identifying AI-growth opportunities to drive outcomes like Predictive Maintenance, Machine Vision and Digital Twins. He is building a Kubernetes based platform that aims at bridging the gap between data science and production software. He led the Innovation team out of Bangalore that incubated video Track-inspection from idea into a commercial Product. Dattaraj has 11 patents in Machine Learning and Computer Vision.

作者簡介(中文翻譯)

DATTARAJ JAGDISH RAO是GE Transportation(現為Wabtec Corporation的一部分)的首席架構師。他在GE工作了19年,曾在全球研究、能源和交通領域工作。目前,他負責全球業務的人工智能(AI)戰略,該戰略包括識別AI增長機會,以推動預測性維護、機器視覺和數字孿生等結果。他正在建立一個基於Kubernetes的平台,旨在彌合數據科學和生產軟件之間的差距。他帶領位於班加羅爾的創新團隊,將視頻檢測從概念孵化成商業產品。Dattaraj在機器學習和計算機視覺方面擁有11項專利。