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

Introduction to Deep Learning

Deep learning has revolutionized the ML domain recently and is constantly outperforming classical statistical approaches, and even humans, in various tasks such as image classification, object detection, segmentation, speech transcription, text translation, text understanding, sales forecasting, and much more. In contrast to classical models, DL models use many millions of parameters, parameter sharing, optimization techniques, and implicit feature extraction to outperform all previously hand-crafted feature detectors and ML models when trained with enough data.

In this section, we will help you understand the basics of neural networks and the path to training deeper models with more parameters, better generalization, and hence better performance. This will help you understand how DL-based approaches work, as well as why and when they make sense for certain domains and datasets. If you are already an expert in DL, feel free to skip this section and...