Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services
- Implement end-to-end machine learning pipelines on Azure
- Train deep learning models using Azure compute infrastructure
- Deploy machine learning models using MLOps
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.
What you will learn
- 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