Book Image

Mastering Azure Machine Learning. - Second Edition

By : Christoph Körner, Marcel Alsdorf
Book Image

Mastering Azure Machine Learning. - Second Edition

By: Christoph Körner, Marcel Alsdorf

Overview of this book

Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you’ll be able to combine all the steps you’ve learned by building an MLOps pipeline.
Table of Contents (23 chapters)
Section 1: Introduction to Azure Machine Learning
Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining
Section 3: The Training and Optimization of Machine Learning Models
Section 4: Machine Learning Model Deployment and Operations

Performing data analysis on a tabular dataset

If you haven't followed the steps in Chapter 4, Ingesting Data and Managing Datasets, to download the snapshot of the Melbourne Housing dataset from Kaggle (, please do this before continuing with this section. In the end, you should have the raw dataset file, melb_data.csv, in the mlfiles container in your storage account and have this connected to a datastore called mldemoblob in your Azure Machine Learning workspace.

In the following sections, we will explore the dataset, do some basic statistical analysis, find missing values and outliers, find correlations between features, and take an initial measurement of feature importance while utilizing a random forest model, as we saw in the Visualizing feature and label dependency for classification section of this chapter. You can either create a new Jupyter notebook and follow along with this book or open the 06_ dataprep_melbhousing...