Christoph Korner - Mastering Azure Machine Learning: Perform large-scale end-to-end advanced machine learning on the cloud with Microsoft Azure ML
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Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes
Key Features- Make sense of data on the cloud by implementing advanced analytics
- Train and optimize advanced deep learning models efficiently on Spark using Azure Databricks
- Deploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)
The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud.
The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure ML and takes you through the process of data experimentation, data preparation, and feature engineering using Azure ML and Python. Youll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. Youll also explore how to train, optimize, and tune models using Azure AutoML and HyperDrive, and perform distributed training on Azure ML. Then, youll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure ML, along with the basics of MLOpsDevOps for ML to automate your ML process as CI/CD pipeline.
By the end of this book, youll have mastered Azure ML and be able to confidently design, build and operate scalable ML pipelines in Azure.
What you will learn- Setup your Azure ML workspace for data experimentation and visualization
- Perform ETL, data preparation, and feature extraction using Azure best practices
- Implement advanced feature extraction using NLP and word embeddings
- Train gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure ML
- Use hyperparameter tuning and AutoML to optimize your ML models
- Employ distributed ML on GPU clusters using Horovod in Azure ML
- Deploy, operate and manage your ML models at scale
- Automated your end-to-end ML process as CI/CD pipelines for MLOps
This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.
Table of Contents- Building an End-to-end Machine Learning Pipeline
- Choosing a Machine Learning Service in Azure
- Data Experimentation and Visualization using Azure
- ETL, Data Preparation and Feature Extraction
- Advanced Feature Extraction with NLP
- Building ML Models using Azure Machine Learning
- Training Deep Neural Networks on Azure
- Hyperparameter Tuning and Automated Machine Learning
- Distributed Machine Learning on Azure ML Clusters
- Building a Recommendation Engine in Azure
- Deploying and Operating Machine Learning Models
- MLOps DevOps for Machine Learning
- Whats next?
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Copyright 2020 Packt Publishing
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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
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Christoph Krner recently worked as a cloud solution architect for Microsoft, specialising in Azure-based big data and machine learning solutions, where he was responsible to design end-to-end machine learning and data science platforms. For the last few months, he has been working as a senior software engineer at HubSpot, building a large-scale analytics platform. Before Microsoft, Christoph was the technical lead for big data at T-Mobile, where his team designed, implemented, and operated large-scale data analytics and prediction pipelines on Hadoop. He has also authored three books: Deep Learning in the Browser (for Bleeding Edge Press), Learning Responsive Data Visualization, and Data Visualization with D3 and AngularJS (both for Packt).
Kaijisse Waaijer is an experienced technologist specializing in data platforms, machine learning, and the Internet of Things. Kaijisse currently works for Microsoft EMEA as a data platform consultant specializing in data science, machine learning, and big data. She works constantly with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data to create better outcomes and business insights that drive value using Microsoft technologies. Her true passion lies within the trading systems automation and applying deep learning and neural networks to achieve advanced levels of prediction and automation.
Alexey Bokov is an experienced Azure architect and Microsoft technical evangelist since 2011. He works closely with Microsoft's top-tier customers all around the world to develop applications based on the Azure cloud platform. Building cloud-based applications for challenging scenarios is his passion, along with helping the development community to upskill and learn new things through hands-on exercises and hacking. He's a long-time contributor to, and coauthor and reviewer of, many Azure books, and, from time to time, is a speaker at Kubernetes events.
Marek Chmel is a Sr. Cloud Solutions Architect at Microsoft for Data & Artificial Intelligence , speaker and trainer with more than 15 years' experience. He's a frequent conference speaker, focusing on SQL Server, Azure and security topics. He has been a Data Platform MVP since 2012 for 8 years. He has earned numerous certifications, including MCSE: Data Management and Analytics, Azure Architect, Data Engineer and Data Scientist Associate, EC Council Certified Ethical Hacker, and several eLearnSecurity certifications. Marek earned his MSc degree in business and informatics from Nottingham Trent University. He started his career as a trainer for Microsoft Server courses and later worked as Principal SharePoint and Principal Database Administrator.
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To make use of the current increase in the volume of data being generated globally requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy models. This book will help you improve your knowledge of building machine learning (ML) models using Azure and end-to-end ML pipelines on the cloud.
The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure ML and takes you through the process of data experimentation, data preparation, and feature engineering. You'll learn advanced feature extraction techniques using
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