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Overview
In MLOps Engineering at Scale you will learn:
Extracting, transforming, and loading datasets
Querying datasets with SQL
Understanding automatic differentiation in PyTorch
Deploying model training pipelines as a service endpoint
Monitoring and managing your pipeline’s life cycle
Measuring performance improvements
MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities.
About the technology
A production-ready machine learning system includes efficient data pipelines, integrated monitoring, and means to scale up and down based on demand. Using cloud-based services to implement ML infrastructure reduces development time and lowers hosting costs. Serverless MLOps eliminates the need to build and maintain custom infrastructure, so you can concentrate on your data, models, and algorithms.
About the book
MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production.
What's inside
Reduce or eliminate ML infrastructure management
Learn state-of-the-art MLOps tools like PyTorch Lightning and MLFlow
Deploy training pipelines as a service endpoint
Monitor and manage your pipeline’s life cycle
Measure performance improvements
About the reader
Readers need to know Python, SQL, and the basics of machine learning. No cloud experience required.
About the author
Carl Osipov implemented his first neural net in 2000 and has worked on deep learning and machine learning at Google and IBM.
Table of Contents
PART 1 - MASTERING THE DATA SET
1 Introduction to serverless machine learning
2 Getting started with the data set
3 Exploring and preparing the data set
4 More exploratory data analysis and data preparation
PART 2 - PYTORCH FOR SERVERLESS MACHINE LEARNING
5 Introducing PyTorch: Tensor basics
6 Core PyTorch: Autograd, optimizers, and utilities
7 Serverless machine learning at scale
8 Scaling out with distributed training
PART 3 - SERVERLESS MACHINE LEARNING PIPELINE
9 Feature selection
10 Adopting PyTorch Lightning
11 Hyperparameter optimization
12 Machine learning pipeline
Product Details
| ISBN-13: | 9781638356509 |
|---|---|
| Publisher: | Manning |
| Publication date: | 03/22/2022 |
| Sold by: | SIMON & SCHUSTER |
| Format: | eBook |
| Pages: | 344 |
| File size: | 5 MB |
About the Author
Table of Contents
Preface xiii
Acknowledgments xv
About this book xvii
About the author xxi
About the cover illustration xxii
Part 1 Mastering the data set 1
1 Introduction to serverless machine learning 3
1.1 What is a machine learning platform? 5
1.2 Challenges when designing a machine learning platform 5
1.3 Public clouds for machine learning platforms 7
1.4 What is serverless machine learning? 8
1.5 Why serverless machine learning? 8
Serverless vs. laaS and PaaS 10
Serverless machine learning life cycle 11
1.6 Who is this book for? 11
What you can get out of this book 11
1.7 How does this book teach? 12
1.8 When is this book not for you? 13
1.9 Conclusions 14
2 Getting started with the data set 15
2.1 Introducing the Washington, DC, taxi rides data set 16
What is the business use case? 16
What are the business rules? 16
What is the schema for the business service? 17
What are the options for implementing the business service? 18
What data assets are available for the business service? 19
Downloading and unzipping the data set 19
2.2 Starting with object storage for the data set 20
Understanding object storage vs. filesystems 21
Authenticating with Amazon Web Services 22
Creating a serverless object storage bucket 23
2.3 Discovering the schema for the data set 26
Introducing AWS Glue 26
Authorizing the crawler to access your objects 27
Using a crawler to discover the data schema 28
2.4 Migrating to columnar storage for more efficient analytics 31
Introducing column-oriented data formats for analytics 31
Migrating to a column-oriented data format 33
3 Exploring and preparing the data set 38
3.1 Getting started with interactive querying 39
Choosing the right use case for interactive querying 39
Introducing AWS Athena 40
Preparing a sample data set 42
Interactive querying using Athena from a browser 43
Interactive querying using a sample data set 44
Querying the DC taxi data set 49
3.2 Getting started with data quality 49
From "garbage in, garbage out" to data quality 50
Before starting with data quality 51
Normative principles for data quality 52
3.3 Applying VACUUM to the DC taxi data 58
Enforcing the schema to ensure valid values 59
Cleaning up invalid fare amounts 63
Improving the accuracy 66
3.4 Implementing VACUUM in a PySpark job 74
4 More exploratory data analysis and data preparation 81
4.1 Getting started with data sampling 82
Exploring the summary statistics of the cleaned-up data set 82
Choosing the right sample size for the test data set 86
Exploring the statistics of alternative sample sizes 88
Using a PySpark job to sample the test set 92
Part 2 PyTorch for serverless machine learning 101
5 Introducing PyTorch: Tensor basics 103
5.1 Getting started with tensors 104
5.2 Getting started with PyTorch tensor creation operations 108
5.3 Creating PyTorch tensors of pseudorandom and interval values 110
5.4 PyTorch tensor operations and broadcasting 112
5.5 PyTorch tensors vs. native Python lists 116
6 Core PyTorch: Autograd, optimizers, and utilities 120
6.1 Understanding the basics of autodiff 121
6.2 Linear regression using PyTorch automatic differentiation 129
6.3 Transitioning to PyTorch optimizers for gradient descent 132
6.4 Getting started with data set batches for gradient descent 135
6.5 Data set batches with PyTorch Dataset and DataLoader 136
6.6 Dataset and DataLoader classes for gradient descent with batches 140
7 Serverless machine learning at scale 143
7.1 What if a single node is enough for my machine learning model? 144
7.2 Using IterableDataset and ObjectStorageDataset 145
7.3 Gradient descent with out-of-memory data sets 149
7.4 Faster PyTorch tensor operations with GPUs 154
7.5 Scaling up to use GPU cores 159
8 Scaling out with distributed training 162
8.1 What if the training data set does not fit in memory? 163
Illustrating gradient accumulation 163
Preparing a sample model and data set 164
Understanding gradient descent using out-of-memory data shards 166
8.2 Parameter server approach to gradient accumulation 169
8.3 Introducing logical ring-based gradient descent 170
8.4 Understanding ring-based distributed gradient descent 174
8.5 Phase 1: Reduce-scatter 176
8.6 Phase 2: All-gather 181
Part 3 Serverless machine learning pipeline 189
9 Feature selection 191
9.1 Guiding principles for feature selection 192
Related to the label 192
Recorded before inference time 194
Supported by abundant examples 196
Expressed as a number with a meaningful scale 197
Based on expert insights about the project 198
9.2 Feature selection case studies 198
9.3 Feature selection using guiding principles 199
Related to the label 199
Recorded before inference time 203
Supported by abundant examples 205
Numeric with meaningful magnitude 207
Bring expert insight to the problem 208
9.4 Selecting features for the DC taxi data set 210
10 Adopting Py Torch Lightning 213
10.1 Understanding PyTorch Lightning 213
Converting PyTorch-model training to PyTorch Lightning 214
Enabling test and reporting for a trained model 221
Enabling validation during model training 223
11 Hyperparameter optimization 228
11.1 Hyperparameter optimization with Optuna 229
Understanding loguniform hyperparameters 230
Using categorical and log-uniform hyperparameters 231
11.2 Neural network layers configuration as a hyperparameter 233
11.3 Experimenting with the batch normalization hyperparameter 235
Using Optuna study for hyperparameter optimization 240
Visualizing an HPO study in Optuna 242
12 Machine learning pipeline 245
12.1 Describing the machine learning pipeline 246
12.2 Enabling PyTorch-distributed training support with Kaen 249
Understanding PyTorch-distributed training settings 255
12.3 Unit testing model training in a local Kaen container 257
12.4 Hyperparameter optimization with Optuna 259
Enabling MLFlow support 264
Using HPO for DcTaxiModel in a local Kaen provider 265
Training with the Kaen AWS provider 269
Appendix A Introduction to machine learning 273
Appendix B Getting started with Docker 300
Index 311