book

The Machine Learning Solutions Architect Handbook

Name: The Machine Learning Solutions Architect Handbook
Author: David Ping
ISBN: 9781801072168

by David Ping

January 2022

Intermediate to advanced

442 pages

9h 56m

English

Packt Publishing

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The Machine Learning Solutions Architect Handbook
ContributorsAbout 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
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Chapter 1: Machine Learning and Machine Learning Solutions Architecture
What are AI and ML? Supervised ML Unsupervised ML Reinforcement learningML versus traditional softwareML life cycleBusiness understanding and ML problem framingData understanding and data preparationModel training and evaluationModel deploymentModel monitoringBusiness metric trackingML challengesML solutions architectureBusiness understanding and ML transformation Identification and verification of ML techniques System architecture design and implementationML platform workflow automation Security and complianceTesting your knowledgeSummary
Chapter 2: Business Use Cases for Machine Learning
ML use cases in financial servicesCapital markets front officeCapital markets back office operationsRisk management and fraudInsuranceML use cases in media and entertainmentContent development and productionContent management and discoveryContent distribution and customer engagementML use cases in healthcare and life sciencesMedical imaging analysisDrug discoveryHealthcare data managementML use cases in manufacturingEngineering and product designManufacturing operations – product quality and yieldManufacturing operations – machine maintenanceML use cases in retailProduct search and discoveryTarget marketingSentiment analysisProduct demand forecastingML use case identification exercise Summary
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Chapter 3: Machine Learning Algorithms
Technical requirements How machines learnOverview of ML algorithmsConsideration for choosing ML algorithmsAlgorithms for classification and regression problems Algorithms for time series analysis Algorithms for recommendationAlgorithms for computer vision problemsAlgorithms for natural language processing problemsGenerative modelHands-on exercise Problem statement Dataset descriptionSetting up a Jupyter Notebook environmentRunning the exerciseSummary
Chapter 4: Data Management for Machine Learning
Technical requirementsData management considerations for MLData management architecture for ML Data storage and managementData ingestionData catalogingData processingData versioningML feature storeData serving for client consumptionAuthentication and authorizationData governanceHands-on exercise – data management for ML Creating a data lake using Lake FormationCreating a data ingestion pipelineCreating a Glue catalogDiscovering and querying data in the data lakeCreating an Amazon Glue ETL job to process data for MLBuilding a data pipeline using Glue workflowsSummary
Chapter 5: Open Source Machine Learning Libraries
Technical requirementsCore features of open source machine learning librariesUnderstanding the scikit-learn machine learning libraryInstalling scikit-learnCore components of scikit-learnUnderstanding the Apache Spark ML machine learning libraryInstalling Spark MLCore components of the Spark ML libraryUnderstanding the TensorFlow deep learning libraryInstalling TensorflowCore components of TensorFlowHands-on exercise – training a TensorFlow modelUnderstanding the PyTorch deep learning libraryInstalling PyTorchCore components of PyTorchHands-on exercise – building and training a PyTorch modelSummary
Chapter 6: Kubernetes Container Orchestration Infrastructure Management
Technical requirementsIntroduction to containersKubernetes overview and core conceptsNetworking on KubernetesService meshSecurity and access managementNetwork securityAuthentication and authorization to APIsRunning ML workloads on KubernetesHands-on – creating a Kubernetes infrastructure on AWSProblem statementLab instructionSummary

Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms
Chapter 7: Open Source Machine Learning Platforms
Technical requirementsCore components of an ML platform Open source technologies for building ML platformsUsing Kubeflow for data science environmentsBuilding a model training environmentRegistering models with a model registryServing models using model serving servicesAutomating ML pipeline workflowsHands-on exercise – building a data science architecture using open source technologiesPart 1 – Installing KubeflowPart 2 – tracking experiments and models, and deploying modelsPart 3 – Automating with an ML pipelineSummary
Chapter 8: Building a Data Science Environment Using AWS ML Services
Technical requirementsData science environment architecture using SageMakerSageMaker StudioSageMaker ProcessingSageMaker Training ServiceSageMaker TuningSageMaker ExperimentsSageMaker Hosting Hands-on exercise – building a data science environment using AWS servicesProblem statement DatasetLab instructionsSummary
Chapter 9: Building an Enterprise ML Architecture with AWS ML Services
Technical requirementsKey requirements for an enterprise ML platformEnterprise ML architecture pattern overviewModel training environment Model training engineAutomation support Model training life cycle managementModel hosting environment deep diveInference engineAuthentication and security controlMonitoring and loggingAdopting MLOps for ML workflowsComponents of the MLOps architectureMonitoring and loggingHands-on exercise – building an MLOps pipeline on AWSCreating a CloudFormation template for the ML training pipelineCreating a CloudFormation template for the ML deployment pipelineSummary
Chapter 10: Advanced ML Engineering
Technical requirementsTraining large-scale models with distributed training Distributed model training using data parallelismDistributed model training using model parallelismAchieving low latency model inferenceHow model inference works and opportunities for optimizationHardware accelerationModel optimizationGraph and operator optimizationModel compilersInference engine optimizationHands-on lab – running distributed model training with PyTorch Modifying the training script Modifying and running the launcher notebook Summary
Chapter 11: ML Governance, Bias, Explainability, and Privacy
Technical requirementsWhat is ML governance and why is it needed?The regulatory landscape around model risk managementCommon causes of ML model risksUnderstanding the ML governance frameworkUnderstanding ML bias and explainabilityBias detection and mitigationML explainability techniquesDesigning an ML platform for governanceData and model documentation Model inventoryModel monitoringChange management controlLineage and reproducibilityObservability and auditingSecurity and privacy-preserving MLHands-on lab – detecting bias, model explainability, and training privacy-preserving modelsOverview of the scenarioDetecting bias in the training datasetExplaining feature importance for the trained modelTraining privacy-preserving models
Chapter 12: Building ML Solutions with AWS AI Services
Technical requirementsWhat are AI services?Overview of AWS AI services Amazon ComprehendAmazon Textract Amazon RekognitionAmazon TranscribeAmazon PersonalizeAmazon LexAmazon Kendra Evaluating AWS AI services for ML use casesBuilding intelligent solutions with AI services Automating loan document verification and data extractionMedia processing and analysis workflowE-commerce product recommendationCustomer self-service automation with intelligent searchDesigning an MLOps architecture for AI servicesAWS account setup strategy for AI services and MLOpsCode promotion across environmentsMonitoring operational metrics for AI servicesHands-on lab – running ML tasks using AI services Summary
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Content preview from The Machine Learning Solutions Architect Handbook

Chapter 11: ML Governance, Bias, Explainability, and Privacy

So far, you have successfully implemented a machine learning (ML) platform. At this point, you might be thinking that your job is done as an ML Solutions Architect (ML SA) and that the business is ready to deploy models into production. Well, it turns out that there are additional considerations. To put models into production, an organization also needs to put governance control in place to meet both the internal policy and external regulatory requirements. ML governance is usually not the responsibility of an ML SA; however, it is important for an ML SA to be familiar with the regulatory landscape and ML governance framework, especially in regulated industries, such as financial services. ...

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Publisher Resources

ISBN: 9781801072168

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

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IT/Ops

Security

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Business

Soft Skills

The Machine Learning Solutions Architect Handbook

by David Ping

Chapter 11: ML Governance, Bias, Explainability, and Privacy

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.