book

Machine Learning Engineering on AWS

Name: Machine Learning Engineering on AWS
Author: Joshua Arvin Lat
ISBN: 9781803247595

by Joshua Arvin Lat

October 2022

Intermediate to advanced

530 pages

11h 57m

English

Packt Publishing

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Machine Learning Engineering on AWS
Copyright © 2022 Packt PublishingContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your Thoughts
Part 1: Getting Started with Machine Learning Engineering on AWS
Chapter 1: Introduction to ML Engineering on AWS
Technical requirementsWhat is expected from ML engineers?How ML engineers can get the most out of AWSEssential prerequisitesCreating the Cloud9 environmentIncreasing Cloud9’s storageInstalling the Python prerequisitesPreparing the datasetGenerating a synthetic dataset using a deep learning modelExploratory data analysisTrain-test splitUploading the dataset to Amazon S3AutoML with AutoGluonSetting up and installing AutoGluonPerforming your first AutoGluon AutoML experimentGetting started with SageMaker and SageMaker StudioOnboarding with SageMaker StudioAdding a user to an existing SageMaker DomainNo-code machine learning with SageMaker CanvasAutoML with SageMaker AutopilotSummaryFurther reading
Chapter 2: Deep Learning AMIs
Technical requirementsGetting started with Deep Learning AMIsLaunching an EC2 instance using a Deep Learning AMILocating the framework-specific DLAMIChoosing the instance typeEnsuring a default secure configurationLaunching the instance and connecting to it using EC2 Instance ConnectDownloading the sample datasetTraining an ML modelLoading and evaluating the modelCleaning upUnderstanding how AWS pricing works for EC2 instancesUsing multiple smaller instances to reduce the overall cost of running ML workloadsUsing spot instances to reduce the cost of running training jobsSummaryFurther reading
Chapter 3: Deep Learning Containers
Technical requirementsGetting started with AWS Deep Learning ContainersEssential prerequisitesPreparing the Cloud9 environmentDownloading the sample datasetUsing AWS Deep Learning Containers to train an ML modelServerless ML deployment with Lambda’s container image supportBuilding the custom container imageTesting the container imagePushing the container image to Amazon ECRRunning ML predictions on AWS LambdaCompleting and testing the serverless API setupSummaryFurther reading
Part 2:Solving Data Engineering and Analysis Requirements
Chapter 4: Serverless Data Management on AWS
Technical requirementsGetting started with serverless data managementPreparing the essential prerequisitesOpening a text editor on your local machineCreating an IAM userCreating a new VPCUploading the dataset to S3Running analytics at scale with Amazon Redshift ServerlessSetting up a Redshift Serverless endpointOpening Redshift query editor v2Creating a tableLoading data from S3Querying the databaseUnloading data to S3Setting up Lake FormationCreating a databaseCreating a table using an AWS Glue CrawlerUsing Amazon Athena to query data in Amazon S3Setting up the query result locationRunning SQL queries using AthenaSummaryFurther reading
Chapter 5: Pragmatic Data Processing and Analysis
Technical requirementsGetting started with data processing and analysisPreparing the essential prerequisitesDownloading the Parquet file Preparing the S3 bucketAutomating data preparation and analysis with AWS Glue DataBrewCreating a new datasetCreating and running a profile jobCreating a project and configuring a recipeCreating and running a recipe jobVerifying the resultsPreparing ML data with Amazon SageMaker Data WranglerAccessing Data WranglerImporting dataTransforming the dataAnalyzing the dataExporting the data flowTurning off the resourcesVerifying the resultsSummaryFurther reading
Part 3: Diving Deeper with Relevant Model Training and Deployment Solutions

Chapter 6: SageMaker Training and Debugging Solutions
Technical requirementsGetting started with the SageMaker Python SDKPreparing the essential prerequisitesCreating a service limit increase requestTraining an image classification model with the SageMaker Python SDKCreating a new Notebook in SageMaker StudioDownloading the training, validation, and test datasetsUploading the data to S3Using the SageMaker Python SDK to train an ML modelUsing the %store magic to store dataUsing the SageMaker Python SDK to deploy an ML modelUsing the Debugger Insights DashboardUtilizing Managed Spot Training and CheckpointsCleaning upSummaryFurther reading
Chapter 7: SageMaker Deployment Solutions
Technical requirementsGetting started with model deployments in SageMakerPreparing the pre-trained model artifacts Preparing the SageMaker script mode prerequisitesPreparing the inference.py filePreparing the requirements.txt filePreparing the setup.py fileDeploying a pre-trained model to a real-time inference endpointDeploying a pre-trained model to a serverless inference endpointDeploying a pre-trained model to an asynchronous inference endpointCreating the input JSON fileAdding an artificial delay to the inference scriptDeploying and testing an asynchronous inference endpointCleaning upDeployment strategies and best practices SummaryFurther reading
Part 4:Securing, Monitoring, and Managing Machine Learning Systems and Environments
Chapter 8: Model Monitoring and Management Solutions
Technical prerequisitesRegistering models to SageMaker Model RegistryCreating a new notebook in SageMaker StudioRegistering models to SageMaker Model Registry using the boto3 libraryDeploying models from SageMaker Model RegistryEnabling data capture and simulating predictionsScheduled monitoring with SageMaker Model MonitorAnalyzing the captured dataDeleting an endpoint with a monitoring scheduleCleaning upSummaryFurther reading
Chapter 9: Security, Governance, and Compliance Strategies
Managing the security and compliance of ML environmentsAuthentication and authorizationNetwork securityEncryption at rest and in transitManaging compliance reportsVulnerability managementPreserving data privacy and model privacyFederated LearningDifferential PrivacyPrivacy-preserving machine learningOther solutions and optionsEstablishing ML governanceLineage Tracking and reproducibilityModel inventoryModel validationML explainabilityBias detectionModel monitoringTraceability, observability, and auditingData quality analysis and reportingData integrity managementSummaryFurther reading
Part 5:Designing and Building End-to-end MLOps Pipelines
Chapter 10: Machine Learning Pipelines with Kubeflow on Amazon EKS
Technical requirementsDiving deeper into Kubeflow, Kubernetes, and EKSPreparing the essential prerequisitesPreparing the IAM role for the EC2 instance of the Cloud9 environmentAttaching the IAM role to the EC2 instance of the Cloud9 environmentUpdating the Cloud9 environment with the essential prerequisitesSetting up Kubeflow on Amazon EKSRunning our first Kubeflow pipelineUsing the Kubeflow Pipelines SDK to build ML workflowsCleaning upRecommended strategies and best practicesSummaryFurther reading
Chapter 11: Machine Learning Pipelines with SageMaker Pipelines
Technical requirementsDiving deeper into SageMaker PipelinesPreparing the essential prerequisitesRunning our first pipeline with SageMaker PipelinesDefining and preparing our first ML pipelineRunning our first ML pipelineCreating Lambda functions for deploymentPreparing the Lambda function for deploying a model to a new endpointPreparing the Lambda function for checking whether an endpoint existsPreparing the Lambda function for deploying a model to an existing endpointTesting our ML inference endpointCompleting the end-to-end ML pipelineDefining and preparing the complete ML pipelineRunning the complete ML pipelineCleaning upRecommended strategies and best practicesSummaryFurther reading
Index
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Content preview from Machine Learning Engineering on AWS

10 Machine Learning Pipelines with Kubeflow on Amazon EKS

In Chapter 9, Security, Governance, and Compliance Strategies, we discussed a lot of concepts and solutions that focus on the other challenges and issues we need to worry about when dealing with machine learning (ML) requirements. You have probably realized by now that ML practitioners have a lot of responsibilities and work to do outside model training and deployment! Once a model gets deployed into production, we would have to monitor the model and ensure that we are able to detect and manage a variety of issues. In addition to this, ML engineers might need to build ML pipelines to automate the different steps in the ML life cycle. To ensure that we reliably deploy ML models in production, ...

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Publisher Resources

ISBN: 9781803247595

Cloud Computing

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Data Science

AI & ML

Programming Languages

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IT/Ops

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Machine Learning Engineering on AWS

by Joshua Arvin Lat

10

Machine Learning Pipelines with Kubeflow on Amazon EKS

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.