Book Image

Mastering Azure Machine Learning - Second Edition

By : Körner, Alsdorf
Book Image

Mastering Azure Machine Learning - Second Edition

By: Körner, 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)
1
Section 1: Introduction to Azure Machine Learning
5
Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining
11
Section 3: The Training and Optimization of Machine Learning Models
17
Section 4: Machine Learning Model Deployment and Operations

Summary

In the first two parts of this chapter, you learned what techniques exist for you to explore and statistically analyze raw datasets and how to use them hands-on on a real-life dataset.

After that, you learned about the dimensionality reduction techniques you can use to visualize high-dimensional datasets. There, you learned about techniques that are extremely useful for you to understand your data, its principal components, discriminant directions, and separability.

Furthermore, everything you have learned in this chapter can be performed on a compute cluster in your Azure Machine Learning workspace, through which you can keep track of all the figures and outputs that are generated.

In the next chapter, using all the knowledge you've gained so far, you will dive into the topic of feature engineering, where you learn how to select and transform features in datasets to prepare them for ML training. In addition, you will have a closer look at labeling and how Azure...