Mastering Azure Machine Learning: Execute large-scale end-to-end machine learning with Azure, 2nd Edition
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Packt Publishing
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.
About the Author
Christoph Krner previously worked as a cloud solution architect for Microsoft, specializing in Azure-based big data and machine learning solutions, where he was responsible for designing end-to-end machine learning and data science platforms. He currently works for a large cloud provider on highly scalable distributed in-memory database services. Christoph has authored four books: Deep Learning in the Browser for Bleeding Edge Press, as well as Mastering Azure Machine Learning (first edition), Learning Responsive Data Visualization, and Data Visualization with D3 and AngularJS for Packt Publishing.
Marcel Alsdorf is a cloud solution architect with 5 years of experience at Microsoft consulting various companies on their cloud strategy. In this role, he focuses on supporting companies in their move toward being data-driven by analyzing their requirements and designing their data infrastructure in the areas of IoT and event streaming, data warehousing, and machine learning. On the side, he shares his technical and business knowledge as a coach in hackathons, as a mentor for start-ups and peers, and as a university lecturer. Before his current role, he worked as an FPGA engineer for the LHC project at CERN and as a software engineer in the banking industry.
--This text refers to the paperback edition.- Understand the end-to-end ML pipeline
- Get to grips with the Azure Machine Learning workspace
- Ingest, analyze, and preprocess datasets for ML using the Azure cloud
- Train traditional and modern ML techniques efficiently using Azure ML
- Deploy ML models for batch and real-time scoring
- Understand model interoperability with ONNX
- Deploy ML models to FPGAs and Azure IoT Edge
- Build an automated MLOps pipeline using Azure DevOps
This book is for machine learning engineers, data scientists, and machine learning developers who want to use the Microsoft Azure cloud to manage their datasets and machine learning experiments and build an enterprise-grade ML architecture using MLOps. This book will also help anyone interested in machine learning to explore important steps of the ML process and use Azure Machine Learning to support them, along with building powerful ML cloud applications. A basic understanding of Python and knowledge of machine learning are recommended.
- Understanding the End-to-End Machine Learning Process
- Choosing the Right Machine Learning Service in Azure
- Preparing the Azure Machine Learning Workspace
- Ingesting Data and Managing Datasets
- Performing Data Analysis and Visualization
- Feature Engineering and Labeling
- Advanced Feature Extraction with NLP
- Azure Machine Learning Pipelines
- Building ML Models Using Azure Machine Learning
- Training Deep Neural Networks on Azure
- Hyperparameter Tuning and Automated Machine Learning
- Distributed Machine Learning on Azure
- Building a Recommendation Engine in Azure
- Model Deployment, Endpoints, and Operations
- Model Interoperability, Hardware Optimization, and Integrations
- Bringing Models into Production with MLOps
- Preparing for a Successful ML Journey
