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Mastering Azure Machine Learning
Mastering Azure Machine Learning

Mastering Azure Machine Learning: Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning

By Christoph Körner , Kaijisse Waaijer
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Book Apr 2020 436 pages 1st Edition
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Product Details

Publication date : Apr 30, 2020
Length 436 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789807554
Vendor :
Microsoft
Category :
Data
Languages :
C# (Beginner)
Concepts :
Machine Learning
Tools :
Azure (Beginner)
Table of content icon View table of contents Preview book icon Preview Book

Mastering Azure Machine Learning

1. Building an end-to-end machine learning pipeline in Azure

This first chapter covers all the required components for running a custom end-to-end machine learning (ML) pipeline in Azure. Some sections might be a recap of your existing knowledge with useful practical tips, step-by-step guidelines, and pointers to using Azure services to perform ML at scale. You can see it as an overview of the book, where we will dive into each section in great detail with many practical examples and a lot of code during the remaining chapters of the book.

First, we will look at data experimentation techniques as a step-by-step process for analyzing common insights, such as missing values, data distribution, feature importance, and two-dimensional embedding techniques to estimate the expected model performance of a classification task. In the second section, we will use these insights about the data to perform data preprocessing and feature engineering, such as normalization, the encoding...

Performing descriptive data exploration

Descriptive data exploration is, without a doubt, one of the most important steps in an ML project. If you want to clean data and build derived features or select an ML algorithm to predict a target variable in your dataset, then you need to understand your data first. Your data will define many of the necessary cleaning and preprocessing steps; it will define which algorithms you can choose and it will ultimately define the performance of your predictive model.

Hence, data exploration should be considered an important analytical step to understanding whether your data is informative to build an ML model in the first place. By analytical step, we mean that the exploration should be done as a structured analytical process rather than a set of experimental tasks. Therefore, we will go through a checklist of data exploration tasks that you can perform as an initial step in every ML project—before starting any data cleaning, preprocessing...

Exploring common techniques for data preparation

After the data experimentation phase, you should have gathered enough knowledge to start preprocessing the data. This process is also often referred to as feature engineering. When coming from multiple sources, such as applications, databases, or warehouses, as well as external sources, your data cannot be analyzed or interpreted immediately.

It is, therefore, of imminent importance to preprocess data before you choose a model to interpret your problem. In addition to this, there are different steps involved in data preparation, which depend on the data that is available to you, such as the problem you want to solve, and with that, the ML algorithms that could be used for it.

You might ask yourself why data preparation is so important. The answer is that the preparation of your data might lead to improvements in model accuracy when done properly. This could be due to the relationships within your data that have been simplified...

Choosing the right ML model to train data

Similar to data experimentation and preprocessing, training ML model is an analytical, step-by-step process. Each step involves a thought process that evaluates the pros and cons of each algorithm according to the results of the experimentation phase. Like in every other scientific process, it is recommended that you come up with a hypothesis first and verify whether this hypothesis is true afterward.

Let's look at the steps that define the process of training an ML model:

  • Define your ML task: First, we need to define the ML task we are facing, which most of the time is defined by the business decision behind your use case. Depending on the amount of labeled data, you can choose between non-supervised, semi-supervised, and supervised learning, as well as many other subcategories.
  • Pick a suitable model to perform this task: Pick a suitable model for the chosen ML task. This includes logistic regression, a gradient-boosted...

Optimization techniques

If we have trained a simple ensemble model that performs reasonably better than the baseline model and achieves acceptable performance according to the expected performance estimated during data preparation, we can progress with optimization. This is a point we really want to emphasize. It's strongly discouraged to begin model optimization and stacking when a simple ensemble technique fails to deliver useful results. If this is the case, it would be much better to take a step back and dive deeper into data analysis and feature engineering.

Common ML optimization techniques, such as hyperparameter optimization, model stacking, and even automated machine learning, help you get the last 10% of performance boost out of your model while the remaining 90% is achieved by a single ensemble model. If you decide to use any of those optimization techniques, it is advised to perform them in parallel and fully automated on a distributed cluster.

After seeing too...

Deploying and operating models

Once you have trained and optimized an ML model, it is ready for deployment. Many data science teams, in practice, stop here and move the model to production as a Docker image, often embedded in a REST API using Flask or similar frameworks. However, as you can imagine, this is not always the best solution depending on your use case requirements. An ML or data engineer's responsibility doesn't stop here.

The deployment and operation of an ML pipeline can be best seen when testing the model on live data in production. A test is done to collect insights and data to continuously improve the model. Hence, collecting model performance over time is an essential step to guaranteeing and improving the performance of the model.

In general, we differentiate two architectures for ML-scoring pipelines, which we will briefly discuss in this section:

  • Batch scoring using pipelines
  • Real-time scoring using a container-based web service
...

Summary

In this chapter, we saw an overview of all the steps involved in making a custom ML pipeline. You might have seen familiar concepts for data preprocessing or analytics and learned an important lesson. Data experimentation is a step-by-step approach rather than an experimental process. Look for missing values, data distribution, and relationships between features and targets. This analysis will greatly help you to understand which preprocessing steps to perform and what model performance to expect.

You now know that data preprocessing, or feature engineering, is the most important part of the whole ML process. The more prior knowledge you have about the data, the better you can encode categorical and temporal variables or transform text to numerical space using NLP techniques. You learned that choosing the proper ML task, model, error metric, and train-test split is mostly defined by business decisions (for example, object detection against segmentation) or a performance...

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Key benefits

  • Make sense of data on the cloud by implementing advanced analytics
  • Train and optimize advanced deep learning models efficiently on Spark using Azure Databricks
  • Deploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)

Description

The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure.

What you will learn

Setup your Azure Machine Learning workspace for data experimentation and visualization Perform ETL, data preparation, and feature extraction using Azure best practices Implement advanced feature extraction using NLP and word embeddings Train gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine Learning Use hyperparameter tuning and Azure Automated Machine Learning to optimize your ML models Employ distributed ML on GPU clusters using Horovod in Azure Machine Learning Deploy, operate and manage your ML models at scale Automated your end-to-end ML process as CI/CD pipelines for MLOps

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Product Details

Publication date : Apr 30, 2020
Length 436 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789807554
Vendor :
Microsoft
Category :
Data
Languages :
C# (Beginner)
Concepts :
Machine Learning
Tools :
Azure (Beginner)

Table of Contents

20 Chapters
Preface Chevron down icon Chevron up icon
Preface
About Mastering Azure Machine Learning
Section 1: Azure Machine Learning Chevron down icon Chevron up icon
Section 1: Azure Machine Learning
1. Building an end-to-end machine learning pipeline in Azure Chevron down icon Chevron up icon
1. Building an end-to-end machine learning pipeline in Azure
Performing descriptive data exploration
Exploring common techniques for data preparation
Choosing the right ML model to train data
Optimization techniques
Deploying and operating models
Summary
2. Choosing a machine learning service in Azure Chevron down icon Chevron up icon
2. Choosing a machine learning service in Azure
Demystifying the different Azure services for ML
Azure Cognitive Services and Custom Vision
Azure Machine Learning with GUIs
Azure Machine Learning workspace
Summary
Section 2: Experimentation and Data Preparation Chevron down icon Chevron up icon
Section 2: Experimentation and Data Preparation
3. Data experimentation and visualization using Azure Chevron down icon Chevron up icon
3. Data experimentation and visualization using Azure
Preparing your Azure Machine Learning workspace
Visualizing high-dimensional data
Summary
4. ETL, data preparation, and feature extraction Chevron down icon Chevron up icon
4. ETL, data preparation, and feature extraction
Managing data and datasets in the cloud
Preprocessing and feature engineering with Azure Machine Learning DataPrep
Summary
5. Azure Machine Learning pipelines Chevron down icon Chevron up icon
5. Azure Machine Learning pipelines
Benefits of pipelines for ML workflows
Building and publishing an ML pipeline
Integrating pipelines with other Azure services
Summary
6. Advanced feature extraction with NLP Chevron down icon Chevron up icon
6. Advanced feature extraction with NLP
Understanding categorical data
Building a simple bag-of-words model
Leveraging term importance and semantics
Implementing end-to-end language models
Summary
Section 3: Training Machine Learning Models Chevron down icon Chevron up icon
Section 3: Training Machine Learning Models
7. Building ML models using Azure Machine Learning Chevron down icon Chevron up icon
7. Building ML models using Azure Machine Learning
Working with tree-based ensemble classifiers
Training an ensemble classifier model using LightGBM
Summary
8. Training deep neural networks on Azure Chevron down icon Chevron up icon
8. Training deep neural networks on Azure
Introduction to deep learning
Training a CNN for image classification
Summary
9. Hyperparameter tuning and Automated Machine Learning Chevron down icon Chevron up icon
9. Hyperparameter tuning and Automated Machine Learning
Hyperparameter tuning to find the optimal parameters
Finding the optimal model with Azure Automated Machine Learning
Summary
10. Distributed machine learning on Azure Chevron down icon Chevron up icon
10. Distributed machine learning on Azure
Exploring methods for distributed ML
Using distributed ML in Azure
Summary
11. Building a recommendation engine in Azure Chevron down icon Chevron up icon
11. Building a recommendation engine in Azure
Introduction to recommender engines
Content-based recommendations
Collaborative filtering—a rating-based recommendation engine
Combining content and ratings in hybrid recommendation engines
Automatic optimization through reinforcement learning
Summary
Section 4: Optimization and Deployment of Machine Learning Models Chevron down icon Chevron up icon
Section 4: Optimization and Deployment of Machine Learning Models
12. Deploying and operating machine learning models Chevron down icon Chevron up icon
12. Deploying and operating machine learning models
Deploying ML models in Azure
Building a real-time scoring service
Implementing a batch scoring pipeline
Inference optimizations and alternative deployment targets
Monitoring Azure Machine Learning deployments
Summary
13. MLOps—DevOps for machine learning Chevron down icon Chevron up icon
13. MLOps—DevOps for machine learning
Ensuring reproducible builds and deployments
Validating your code, data, and models
Summary
14. What's next? Chevron down icon Chevron up icon
14. What's next?
Understanding the importance of data
The future of ML is automated
Change is the only constant – preparing for change
Focusing first on infrastructure and monitoring
Controlled rollouts and A/B testing
Summary
Index Chevron down icon Chevron up icon
Index

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Authors (4)

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Profile icon Christoph Körner
LinkedIn Github
Christoph Körner 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.
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Profile icon Erik Hanchett
Erik Hanchett is a software developer, blogger, and perpetual student who has been writing code for over 10 years. He currently resides in Reno Nevada, with his wife and two kids. He blogs about software development at ProgramWithErik.com. I would like to thank my wife Susan for helping me stay motivated. My friend F.B. Woods for all his help on the English language and Dr. Bret Simmons for teaching me the value of a personal brand. I would also like to thank all my friends and family that encouraged me along the way.
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Profile icon Kaijisse Waaijer
Kaijisse Waaijer is an experienced technologist specializing in data platforms, machine learning, and the Internet of Things. Kaijisse currently works for Microsoft EMEA as a data platform consultant specializing in data science, machine learning, and big data. She works constantly with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data to create better outcomes and business insights that drive value using Microsoft technologies. Her true passion lies within the trading systems automation and applying deep learning and neural networks to achieve advanced levels of prediction and automation.
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Profile icon Kaijisse Waaijer
Kaijisse Waaijer is an experienced technologist specializing in data platforms, machine learning, and the Internet of Things. Kaijisse currently works for Microsoft EMEA as a data platform consultant specializing in data science, machine learning, and big data. She works constantly with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data to create better outcomes and business insights that drive value using Microsoft technologies. Her true passion lies within the trading systems automation and applying deep learning and neural networks to achieve advanced levels of prediction and automation.
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