Machine learning engineering on AWS : Machine learning engineering on AWS : build, scale, and secure machine learning systems and MLOps pipelines in production / Joshua Arvin Lat.

Format:

Book

Author/Creator:

Lat, Joshua Arvin, author.

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (530 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2022]

Summary:

Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle Key Features Gain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and more Use container and serverless services to solve a variety of ML engineering requirements Design, build, and secure automated MLOps pipelines and workflows on AWS Book Description There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS. By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements. What you will learn Find out how to train and deploy TensorFlow and PyTorch models on AWS Use containers and serverless services for ML engineering requirements Discover how to set up a serverless data warehouse and data lake on AWS Build automated end-to-end MLOps pipelines using a variety of services Use AWS Glue DataBrew and SageMaker Data Wrangler for data engineering Explore different solutions for deploying deep learning models on AWS Apply cost optimization techniques to ML environments and systems Preserve data privacy and model privacy using a variety of techniques Who this book is for This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Part 1: Getting Started with Machine Learning Engineering on AWS

Chapter 1: Introduction to ML Engineering on AWS

Technical requirements

What is expected from ML engineers?

How ML engineers can get the most out of AWS

Essential prerequisites

Creating the Cloud9 environment

Increasing Cloud9's storage

Installing the Python prerequisites

Preparing the dataset

Generating a synthetic dataset using a deep learning model

Exploratory data analysis

Train-test split

Uploading the dataset to Amazon S3

AutoML with AutoGluon

Setting up and installing AutoGluon

Performing your first AutoGluon AutoML experiment

Getting started with SageMaker and SageMaker Studio

Onboarding with SageMaker Studio

Adding a user to an existing SageMaker Domain

No-code machine learning with SageMaker Canvas

AutoML with SageMaker Autopilot

Summary

Further reading

Chapter 2: Deep Learning AMIs

Getting started with Deep Learning AMIs

Launching an EC2 instance using a Deep Learning AMI

Locating the framework-specific DLAMI

Choosing the instance type

Ensuring a default secure configuration

Launching the instance and connecting to it using EC2 Instance Connect

Downloading the sample dataset

Training an ML model

Loading and evaluating the model

Cleaning up

Understanding how AWS pricing works for EC2 instances

Using multiple smaller instances to reduce the overall cost of running ML workloads

Using spot instances to reduce the cost of running training jobs

Chapter 3: Deep Learning Containers

Getting started with AWS Deep Learning Containers

Essential prerequisites.

Preparing the Cloud9 environment

Using AWS Deep Learning Containers to train an ML model

Serverless ML deployment with Lambda's container image support

Building the custom container image

Testing the container image

Pushing the container image to Amazon ECR

Running ML predictions on AWS Lambda

Completing and testing the serverless API setup

Part 2: Solving Data Engineering and Analysis Requirements

Chapter 4: Serverless Data Management on AWS

Getting started with serverless data management

Preparing the essential prerequisites

Opening a text editor on your local machine

Creating an IAM user

Creating a new VPC

Uploading the dataset to S3

Running analytics at scale with Amazon Redshift Serverless

Setting up a Redshift Serverless endpoint

Opening Redshift query editor v2

Creating a table

Loading data from S3

Querying the database

Unloading data to S3

Setting up Lake Formation

Creating a database

Creating a table using an AWS Glue Crawler

Using Amazon Athena to query data in Amazon S3

Setting up the query result location

Running SQL queries using Athena

Chapter 5: Pragmatic Data Processing and Analysis

Getting started with data processing and analysis

Downloading the Parquet file

Preparing the S3 bucket

Automating data preparation and analysis with AWS Glue DataBrew

Creating a new dataset

Creating and running a profile job

Creating a project and configuring a recipe

Creating and running a recipe job

Verifying the results

Preparing ML data with Amazon SageMaker Data Wrangler

Accessing Data Wrangler

Importing data

Transforming the data.

Analyzing the data

Exporting the data flow

Turning off the resources

Part 3: Diving Deeper with Relevant Model Training and Deployment Solutions

Chapter 6: SageMaker Training and Debugging Solutions

Getting started with the SageMaker Python SDK

Creating a service limit increase request

Training an image classification model with the SageMaker Python SDK

Creating a new Notebook in SageMaker Studio

Downloading the training, validation, and test datasets

Uploading the data to S3

Using the SageMaker Python SDK to train an ML model

Using the %store magic to store data

Using the SageMaker Python SDK to deploy an ML model

Using the Debugger Insights Dashboard

Utilizing Managed Spot Training and Checkpoints

Chapter 7: SageMaker Deployment Solutions

Getting started with model deployments in SageMaker

Preparing the pre-trained model artifacts

Preparing the SageMaker script mode prerequisites

Preparing the inference.py file

Preparing the requirements.txt file

Preparing the setup.py file

Deploying a pre-trained model to a real-time inference endpoint

Deploying a pre-trained model to a serverless inference endpoint

Deploying a pre-trained model to an asynchronous inference endpoint

Creating the input JSON file

Adding an artificial delay to the inference script

Deploying and testing an asynchronous inference endpoint

Deployment strategies and best practices

Part 4: Securing, Monitoring, and Managing Machine Learning Systems and Environments

Chapter 8: Model Monitoring and Management Solutions

Technical prerequisites.

Registering models to SageMaker Model Registry

Creating a new notebook in SageMaker Studio

Registering models to SageMaker Model Registry using the boto3 library

Deploying models from SageMaker Model Registry

Enabling data capture and simulating predictions

Scheduled monitoring with SageMaker Model Monitor

Analyzing the captured data

Deleting an endpoint with a monitoring schedule

Chapter 9: Security, Governance, and Compliance Strategies

Managing the security and compliance of ML environments

Authentication and authorization

Network security

Encryption at rest and in transit

Managing compliance reports

Vulnerability management

Preserving data privacy and model privacy

Federated Learning

Differential Privacy

Privacy-preserving machine learning

Other solutions and options

Establishing ML governance

Lineage Tracking and reproducibility

Model inventory

Model validation

ML explainability

Bias detection

Model monitoring

Traceability, observability, and auditing

Data quality analysis and reporting

Data integrity management

Part 5: Designing and Building End-to-end MLOps Pipelines

Chapter 10: Machine Learning Pipelines with Kubeflow on Amazon EKS

Diving deeper into Kubeflow, Kubernetes, and EKS

Preparing the IAM role for the EC2 instance of the Cloud9 environment

Attaching the IAM role to the EC2 instance of the Cloud9 environment

Updating the Cloud9 environment with the essential prerequisites

Setting up Kubeflow on Amazon EKS

Running our first Kubeflow pipeline

Using the Kubeflow Pipelines SDK to build ML workflows

Recommended strategies and best practices

Further reading.

Chapter 11: Machine Learning Pipelines with SageMaker Pipelines

Diving deeper into SageMaker Pipelines

Running our first pipeline with SageMaker Pipelines

Defining and preparing our first ML pipeline

Running our first ML pipeline

Creating Lambda functions for deployment

Preparing the Lambda function for deploying a model to a new endpoint

Preparing the Lambda function for checking whether an endpoint exists

Preparing the Lambda function for deploying a model to an existing endpoint

Testing our ML inference endpoint

Completing the end-to-end ML pipeline

Defining and preparing the complete ML pipeline

Running the complete ML pipeline

Index

Other Books You May Enjoy.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781523151516

152315151X

9781803231389

1803231386

OCLC:

1348491798