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

Chapter 17: Preparing for a Successful ML Journey

Congratulations, you've made it – what an incredible journey you've been on! By now, you should have learned how to preprocess data in the cloud, experiment with ML models, train deep learning models and recommendation engines on auto-scaling clusters, optimize models, and deploy them wherever you want. And you should know how to add a cherry to the top of the cake by operationalizing all of these steps through MLOps.

In this last chapter, we will recap some important revelations we learned during this journey. It's easy to get lost or overwhelmed by technological and algorithmic choices. You could dive deep into modeling, infrastructure, or monitoring without getting any closer to having a good predictive model.

In the first section, we will remind you that ML is mostly about data. Artificial intelligence should probably be called data cleansing and labeling, but of course, this doesn't sound as good...