book

Machine Learning Engineering with Python

by Andrew P. McMahon

November 2021

Intermediate to advanced

276 pages

5h 59m

English

Packt Publishing

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ContributorsAbout the authorAbout the reviewers
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
Technical requirementsDefining a taxonomy of data disciplinesData scientistML engineerData engineerAssembling your teamML engineering in the real worldWhat does an ML solution look like?Why Python?High-level ML system designExample 1: Batch anomaly detection serviceExample 2: Forecasting APIExample 3: Streamed classificationSummary
Technical requirementsSetting up our toolsSetting up an AWS accountConcept to solution in four stepsDiscoverPlayDevelopDeploySummary
Technical requirementsDefining the model factoryDesigning your training systemTraining system design optionsTrain-runTrain-persistRetraining requiredDetecting driftEngineering features for consumptionEngineering categorical featuresEngineering numerical featuresLearning about learning Defining the targetCutting your lossesHierarchies of automationOptimizing hyperparameters AutoMLAuto-sklearnPersisting your modelsBuilding the model factory with pipelinesScikit-learn pipelinesSpark ML pipelinesSummary
Technical requirementsWriting good PythonRecapping the basicsTips and tricksAdhering to standardsWriting good PySparkChoosing a styleObject-oriented programmingFunctional programmingPackaging your codeWhy package?Selecting use cases for packagingDesigning your packageBuilding your packageTesting, logging, and error handlingTestingLoggingError handlingNot reinventing the wheelSummary
Technical requirementsArchitecting systemsExploring the unreasonable effectiveness of patternsSwimming in data lakesMicroservicesEvent-based designsBatchingContainerizingHosting your own microservice on AWSPushing to ECRHosting on ECSCreating a load balancerPipelining 2.0Revisiting CI/CDSummary
Technical requirementsScaling with SparkSpark tips and tricksSpark on the cloudSpinning up serverless infrastructureContainerizing at scale with KubernetesSummary

Technical requirementsUnderstanding the forecasting problemDesigning our forecasting serviceSelecting the toolsExecuting the buildTraining pipeline and forecasterTraining and forecast handlersSummary
Technical requirementsUnderstanding the batch processing problemDesigning an ETML solutionSelecting the toolsInterfacesScaling of modelsScheduling of ETML pipelinesExecuting the buildNot reinventing the wheel in practiceUsing the Gitflow workflowInjecting some engineering practices
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BIRMINGHAM—MUMBAI

Machine Learning Engineering with Python

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