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

Deploying ML models in Azure

Broadly speaking, there are two common approaches to deploying ML models, namely deploying them as synchronous real-time web services and as asynchronous batch-scoring services. Please note that the same model could be deployed as two different services, serving different use cases. The deployment type depends heavily on the batch size and response time of the scoring pattern of the model. Small batch sizes with fast responses require a horizontally scalable real-time web service, whereas large batch sizes and slow response times require horizontally and vertically scalable batch services.

The deployment of a text-understanding model (for example, an entity recognition model or sentiment analysis) could include a real-time web service that evaluates the model whenever a new comment is posted to an app, as well as a batch scorer in another ML pipeline to extract relevant features from training data. With the former, we want to serve each request as quickly...