book

Machine Learning for Streaming Data with Python

Name: Machine Learning for Streaming Data with Python
Author: Joos Korstanje
ISBN: 9781803248363

by Joos Korstanje

July 2022

Beginner to intermediate

258 pages

4h 57m

English

Packt Publishing

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Machine Learning for Streaming Data with Python
ContributorsAbout the authorAbout the reviewer
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: Introduction and Core Concepts of Streaming Data
Chapter 1: An Introduction to Streaming Data
Technical requirementsSetting up a Python environmentA short history of data scienceWorking with streaming dataStreaming data versus batch dataAdvantages of streaming dataExamples of successful implementation of streaming analyticsChallenges of streaming dataHow to get started with streaming dataCommon use cases for streaming dataStreaming versus big dataReal-time data formats and importing an example dataset in PythonSummaryFurther reading
Chapter 2: Architectures for Streaming and Real-Time Machine Learning
Technical requirementsPython environmentDefining your analytics as a functionUnderstanding microservices architectureCommunicating between services through APIsDemystifying the HTTP protocolThe GET requestThe POST requestJSON format for communication between systemsRESTful APIsBuilding a simple API on AWSAPI Gateway in AWSLambda in AWSData-generating process on a local machineImplementing the exampleMore architectural considerationsOther AWS services and other services in general that have the same functionalityBig data tools for real time streamingCalling a big data environment in real timeSummaryFurther reading
Chapter 3: Data Analysis on Streaming Data
Technical requirementsPython environmentDescriptive statistics on streaming dataWhy are descriptive statistics different on streaming data?Introduction to sampling theoryComparing population and samplePopulation parameters and sample statisticsSampling distributionSample size calculations and confidence levelRolling descriptive statistics from streamingExponential weightTracking convergence as an additional KPIOverview of the main descriptive statisticsThe meanThe medianThe modeStandard deviationVarianceQuartiles and interquartile rangeCorrelationsReal-time visualizationsOpening the dashboardComparing Plotly's Dash and other real-time visualization toolsBuilding basic alerting systemsAlerting systems on extreme valuesAlerting systems on process stability (mean and median)Alerting systems on constant variability (std and variance)Basic alerting systems using statistical process controlSummaryFurther reading
Part 2: Exploring Use Cases for Data Streaming
Chapter 4: Online Learning with River
Technical requirementsPython environmentWhat is online machine learning?How is online learning different from regular learning?Advantages of online learningChallenges of online learningTypes of online learningUsing River for online learningTraining an online model with RiverImproving the model evaluationBuilding a multiclass classifier using one-vs-restSummaryFurther reading
Chapter 5: Online Anomaly Detection
Technical requirementsPython environmentDefining anomaly detectionAre outliers a problem?Exploring use cases of anomaly detectionFraud detection in financial institutionsAnomaly detection on your log dataFault detection in manufacturing and production linesHacking detection in computer networks (cyber security)Medical risks in health dataPredictive maintenance and sensor dataComparing anomaly detection and imbalanced classificationThe problem of imbalanced dataThe F1 scoreSMOTE oversamplingAnomaly detection versus classificationAlgorithms for detecting anomalies in RiverThe use of thresholders in River anomaly detectionAnomaly detection algorithm 1 – One-Class SVMAnomaly detection algorithm 2 – Half-Space-TreesGoing further with anomaly detectionSummaryFurther reading
Chapter 6: Online Classification
Technical requirementsPython environmentDefining classificationIdentifying use cases of classificationUse case 1 – email spam classificationUse case 2 – face detection in phone cameraUse case 3 – online marketing ad selectionOverview of classification algorithms in RiverClassification algorithm 1 – LogisticRegressionClassification algorithm 2 – PerceptronClassification algorithm 3 – AdaptiveRandomForestClassifierClassification algorithm 4 – ALMAClassifierClassification algorithm 5 – PAClassifierEvaluating benchmark resultsSummaryFurther reading

Chapter 7: Online Regression
Technical requirementsPython environmentDefining regressionUse cases of regressionUse case 1 – ForecastingUse case 2 – Predicting the number of faulty products in manufacturingOverview of regression algorithms in RiverRegression algorithm 1 – LinearRegressionRegression algorithm 2 – HoeffdingAdaptiveTreeRegressorRegression algorithm 3 – SGTRegressorRegression algorithm 4 – SRPRegressorSummaryFurther reading
Chapter 8: Reinforcement Learning
Technical requirementsPython environmentDefining reinforcement learningComparing online and offline reinforcement learningA more detailed overview of feedback loops in reinforcement learningThe main steps of a reinforcement learning modelMaking the decisionsUpdating the decision rulesExploring Q-learningThe goal of Q-learningParameters of the Q-learning algorithmDeep Q-learningUsing reinforcement learning for streaming dataUse cases of reinforcement learningUse case one – trading systemUse case two – social network ranking systemUse case three – a self-driving carUse case four – chatbotsUse case five – learning gamesImplementing reinforcement learning in PythonSummaryFurther reading
Part 3: Advanced Concepts and Best Practices around Streaming Data
Chapter 9: Drift and Drift Detection
Technical requirementsPython environmentDefining driftThree types of driftIntroducing model explicabilityMeasuring driftMeasuring data driftMeasuring concept driftMeasuring drift in PythonA basic intuitive approach to measuring driftMeasuring drift with robust toolsCounteracting driftOffline learning with retraining strategies against driftOnline learning against driftSummaryFurther reading
Chapter 10: Feature Transformation and Scaling
Technical requirementsPython environmentChallenges of data preparation with streaming dataScaling data for streamingIntroducing scalingAdapting scaling to a streaming contextTransforming features in a streaming contextIntroducing PCAMathematical definition of PCARegular PCA in PythonIncremental PCA for streamingSummaryFurther reading
Chapter 11: Catastrophic Forgetting
Technical requirementsPython environmentIntroducing catastrophic forgettingCatastrophic forgetting in online modelsDetecting catastrophic forgettingUsing Python to detect catastrophic forgettingModel explicability versus catastrophic forgettingExplaining models using linear coefficientsExplaining models using dendrogramsExplaining models using variable importanceSummaryFurther reading
Chapter 12: Conclusion and Best Practices
Going furtherSummary
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Content preview from Machine Learning for Streaming Data with Python

Chapter 9: Drift and Drift Detection

Throughout the previous chapters, you have discovered plenty of ways to build machine learning (ML) models that work in an online manner. They are able to update their learned decision rules from one single observation rather than having to retrain completely as is common in most ML models.

One reason that this is great is streaming, as these models will allow you to work and learn continuously. However, we could argue that a traditional ML model can also predict on a single observation. Even batch learning and offline models can predict a single new observation at a time. To get more insight into the added value of online ML, this chapter will go in depth into drift and drift detection.

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Machine Learning for Streaming Data with Python

by Joos Korstanje

Chapter 9: Drift and Drift Detection

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,
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