More Information
  • 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

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. You'll 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. You'll also explore how to train, optimize, and tune models using Azure AutoML and HyperDrive, and perform distributed training on Azure ML. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure ML, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline.

By the end of this book, you'll have mastered Azure ML and be able to confidently design, build and operate scalable ML pipelines in Azure.

  • 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)
Page Count 394
Course Length 11 hours 49 minutes
ISBN 9781789807554
Date Of Publication 30 Apr 2020


Christoph Körner

Christoph Körner recently worked as a Cloud Solution Architect for Microsoft specialised in Azure-based Big Data and Machine Learning solutions where he was responsible to design end-to-end Machine Learning and Data Science platforms. Since a few months, he works 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 also authored the 3 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

Kaijisse Waaijer is an experienced technologist, specializing in Data Platforms, Machine learning, and IoT. Kaijisse currently works for Microsoft EMEA as a Data Platform Consultant, specializing in Data Science, Machine learning and Big Data. She constantly works with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data creating 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.