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

Finding the optimal model with Automated Machine Learning

Automated Machine Learning is an exciting new trend that many (if not all) cloud providers follow. The aim is to provide a service to users that automatically preprocesses your data, selects an ML model, and trains and optimizes the model to fit your training data to optimize a specified error metric. This will create and train a fully automated end-to-end ML pipeline that only needs your labeled training data and target metric as input. Here is a list of steps that Automated Machine Learning optimizes for you:

  • Data preprocessing
  • Feature engineering
  • Model selection
  • Hyperparameter tuning
  • Model ensembling

While most experienced ML engineers or data scientists would be very cautious about the effectiveness of such an automated approach, it still has a ton of benefits, which will be explained in this section. If you like the idea of hyperparameter tuning, then you will find value in Automated Machine...