Book Image

Mastering Azure Machine Learning

By : Christoph Körner, Kaijisse Waaijer
Book Image

Mastering Azure Machine Learning

By: Christoph Körner, Kaijisse Waaijer

Overview of this book

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 Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning 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 Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, 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 Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure.
Table of Contents (20 chapters)
1
Section 1: Azure Machine Learning
4
Section 2: Experimentation and Data Preparation
9
Section 3: Training Machine Learning Models
15
Section 4: Optimization and Deployment of Machine Learning Models
19
Index

13. MLOps—DevOps for machine learning

In the previous chapter, we covered machine learning (ML) deployments in Azure using automated Azure Machine Learning deployments for real-time scoring services, Azure Pipelines for batch prediction services, and ONNX, FPGAs, and Azure IoT Edge for alternative deployment targets. If you have read all of the chapters preceding this one, you will have seen and implemented a complete end-to-end ML pipeline with data cleansing, preprocessing, labeling, experimentation, model development, training, optimization, and deployment.

Congratulations on making it this far! You now possess all the skills needed to connect the bits and pieces together for MLOps and to create DevOps pipelines for your ML models.

Throughout this book, we have emphasized how every step of the ML training and deployment process can be scripted through Bash, PowerShell, the Python SDK, or any other library wrapping the Azure Machine Learning REST service....