Azure Machine Learning Engineering: Deploy, fine-tune, and optimize ML models using Microsoft Azure
- Length: 362 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2023-01-20
- ISBN-10: 1803239301
- ISBN-13: 9781803239309
- Sales Rank: #3115881 (See Top 100 Books)
Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service
Key Features
- Automate complete machine learning solutions using Microsoft Azure
- Understand how to productionize machine learning models
- Get to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learning
Book Description
Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You’ll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.
Throughout the book, you’ll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You’ll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework.
By the end of this Azure Machine Learning book, you’ll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.
What you will learn
- Train ML models in the Azure Machine Learning service
- Build end-to-end ML pipelines
- Host ML models on real-time scoring endpoints
- Mitigate bias in ML models
- Get the hang of using an MLOps framework to productionize models
- Simplify ML model explainability using the Azure Machine Learning service and Azure Interpret
Who this book is for
Machine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered.
Azure Machine Learning Engineering Contributors About the authors About the reviewers Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Share Your Thoughts Download a free PDF copy of this book Part 1: Training and Tuning Models with the Azure Machine Learning Service Chapter 1: Introducing the Azure Machine Learning Service Technical requirements Building your first AMLS workspace Creating an AMLS workspace through the Azure portal Creating an AMLS workspace through the Azure CLI Creating an AMLS workspace with ARM templates Navigating AMLS Creating a compute for writing code Creating a compute instance through the AMLS GUI Adding a schedule to a compute instance Creating a compute instance through the Azure CLI Creating a compute instance with ARM templates Developing within AMLS Developing Python code with Jupyter Notebook Developing using an AML notebook Connecting AMLS to VS Code Summary Chapter 2: Working with Data in AMLS Technical requirements Azure Machine Learning datastore overview Default datastore review Creating a blob storage account datastore Creating a blob storage account datastore through Azure Machine Learning Studio Creating a blob storage account datastore through the Python SDK Creating a blob storage account datastore through the Azure Machine Learning CLI Creating Azure Machine Learning data assets Creating a data asset using the UI Creating a data asset using the Python SDK Using Azure Machine Learning datasets Read data in a job Summary Chapter 3: Training Machine Learning Models in AMLS Technical requirements Training code-free models with the designer Creating a dataset using the user interface Training on a compute instance Training on a compute cluster Summary Chapter 4: Tuning Your Models with AMLS Technical requirements Understanding model parameters Sampling hyperparameters Understanding sweep jobs Truncation policies Median policies Bandit policies Setting up a sweep job with grid sampling Setting up a sweep job for random sampling Setting up a sweep job for Bayesian sampling Reviewing results of a sweep job Summary Chapter 5: Azure Automated Machine Learning Technical requirements Introduction to Azure AutoML Featurization concepts in AML AutoML using AMLS AutoML using the AML Python SDK Parsing your AutoML results via AMLS and the AML SDK Summary Part 2: Deploying and Explaining Models in AMLS Chapter 6: Deploying ML Models for Real-Time Inferencing Technical requirements Understanding real-time inferencing and batch scoring Deploying an MLflow model with managed online endpoints through AML Studio Deploying an MLflow model with managed online endpoints through the Python SDK V2 Deploying a model with managed online endpoints through the Python SDK v2 Deploying a model for real-time inferencing with managed online endpoints through the Azure CLI v2 Summary Chapter 7: Deploying ML Models for Batch Scoring Technical requirements Deploying a model for batch inferencing using the Studio Deploying a model for batch inferencing through the Python SDK Summary Chapter 8: Responsible AI Responsible AI principles Responsible AI Toolbox overview Responsible AI dashboard Error analysis dashboard Interpretability dashboard Data explorer What-if counterfactuals Fairness Summary Chapter 9: Productionizing Your Workload with MLOps Technical requirements Understanding the MLOps implementation Preparing your MLOps environment Creating a second AML workspace Creating an Azure DevOps organization and project Connecting to your AML workspace Moving code to the Azure DevOps repo Setting up variables in Azure Key Vault Setting up environment variable groups Creating an Azure DevOps environment Setting your Azure DevOps service connections Creating an Azure DevOps pipeline Running an Azure DevOps pipeline Summary Further reading Part 3: Productionizing Your Workload with MLOps Chapter 10: Using Deep Learning in Azure Machine Learning Technical requirements Labeling image data using the Data Labeling feature of Azure Machine Learning Training an object detection model using Azure AutoML Deploying the object detection model to an online endpoint using the Azure ML Python SDK Summary Chapter 11: Using Distributed Training in AMLS Technical requirements Data parallelism Model parallelism Distributed training with PyTorch Distributed training code Creating a training job Python file to process Distributed training with TensorFlow Creating a training job Python file to process Summary Index Why subscribe? Other Books You May Enjoy Packt is searching for authors like you Share Your Thoughts Download a free PDF copy of this book
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