{"id":2781,"date":"2025-10-15T10:24:02","date_gmt":"2025-10-15T10:24:02","guid":{"rendered":"https:\/\/www.niraltek.com\/blog\/?p=2781"},"modified":"2025-10-15T10:31:27","modified_gmt":"2025-10-15T10:31:27","slug":"from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline","status":"publish","type":"post","link":"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/","title":{"rendered":"From Data Chaos to Predictive Clarity Inside the Gen AI Fusion Pipeline"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"2781\" class=\"elementor elementor-2781\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ddf27c5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ddf27c5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7ae67f7\" data-id=\"7ae67f7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3d43bf0 elementor-widget elementor-widget-text-editor\" data-id=\"3d43bf0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.8.0 - 30-10-2022 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#818a91;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#818a91;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>In modern systems, raw data rarely arrives in tidy, well-structured form. It\u2019s messy, asynchronous, multimodal, and often noisy. But when properly fused and refined within a Generative AI (Gen AI) Fusion Pipeline, that chaos becomes the backbone of predictive clarity: the ability to forecast events, anticipate anomalies, and guide decisions with confidence. In this blog, we\u2019ll walk through how that transformation happens \u2014 from messy inputs to robust predictions \u2014 and why it matters.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a0f8aeb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a0f8aeb\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d485c43\" data-id=\"d485c43\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ed6db2b elementor-widget elementor-widget-text-editor\" data-id=\"ed6db2b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4>Why \u201cData Chaos\u201d Is the Starting Point<\/h4>\n<h5>Multimodal sources<\/h5>\n<p>Data arrives from many types of sensors and systems \u2014 imaging, telemetry, environmental sensors, logs, external APIs, user behavior data, satellite feeds, and more. Each has its own:<\/p>\n<ul>\n<li>Format (raster, time series, tabular, unstructured)<\/li>\n<li>Rate (burst, periodic, event-driven)<\/li>\n<li>Quality (missing values, noise, misalignment)<\/li>\n<\/ul>\n<div>&nbsp;<\/div>\n<h5>Temporal &amp; spatial misalignment<\/h5>\n<p>Events happen at different scales. One stream might report every second, another once a minute, another hourly. Spatially, data may come from different coordinate systems or reference frames. Without alignment, the signals can\u2019t be meaningfully combined.<\/p>\n<p><span style=\"color: #212121; font-family: Montserrat, sans-serif; font-size: 20px; font-weight: bold; letter-spacing: -0.05em;\">Data gaps &amp; outliers<\/span><\/p>\n<p>Sensors fail, transmissions drop, or environmental glitches cause anomalies. Without handling them, models may overreact to noise or discard useful signals.<\/p>\n<p><span style=\"color: #212121; font-family: Montserrat, sans-serif; font-size: 20px; font-weight: bold; letter-spacing: -0.05em;\">Scale &amp; volume<\/span><\/p>\n<p>Massive datasets can overwhelm pipelines if not carefully architected. The pipeline needs to scale horizontally, manage memory, and distribute computation.<\/p>\n<p>In short: chaos is inevitable. The art is turning it into clarity.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e1eae91 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e1eae91\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2b248be\" data-id=\"2b248be\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e94a8d6 elementor-widget elementor-widget-text-editor\" data-id=\"e94a8d6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4>The Gen AI Fusion Pipeline: High-Level Architecture<\/h4><p>Here\u2019s a conceptual flow of how the pipeline typically works:<\/p><p><span style=\"letter-spacing: -0.05em; color: #212121; font-family: Montserrat, sans-serif; font-weight: bold;\">Ingestion &amp; Buffering<\/span><\/p><ul><li>Use streaming frameworks (Kafka, Pulsar, AWS Kinesis, etc.)<\/li><li>Introduce buffers and windowing to aggregate asynchronous streams into manageable chunks<\/li><\/ul><h6><span style=\"letter-spacing: -0.05em;\">Preprocessing &amp; Normalization<\/span><\/h6><ul><li><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400;\">Data cleaning: fill gaps, drop duplicates, remove corrupt readings<\/span><\/li><li>Time alignment: resample data to common intervals<\/li><li>Spatial alignment: map to unified coordinate systems<\/li><li>Feature scaling \/ normalization<\/li><\/ul><h6>Feature Engineering &amp; Embedding<\/h6><ul><li><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400;\">Modality-specific feature extraction<\/span><\/li><li>Time series \u2192 rolling statistics, derivatives<\/li><li>Imagery \u2192 spatial features, patches, embeddings<\/li><li>Logs \/ text \u2192 embeddings, topic vectors<\/li><li>Dimensionality reduction, denoising, transformation<\/li><\/ul><h6>Cross-Modal Fusion Layer<\/h6><ul><li><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400;\">Combine embeddings via attention networks, cross-modal transformers, or fusion layers<\/span><\/li><li>Learn weighted importance, context, and interactions across modalities<\/li><\/ul><h6>Predictive \/ Generative Modeling<\/h6><h6><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400;\">Use fusion output to power downstream tasks:<\/span><\/h6><ul><li><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400;\">Forecasting (e.g. time to event, trend prediction)<\/span><\/li><li>Anomaly detection<\/li><li>Decision suggestion or control<\/li><li>Generative simulation (e.g. \u201cwhat-if\u201d modeling)<\/li><\/ul><h6>Prediction Audit &amp; Confidence Scoring<\/h6><ul><li><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400;\">Assess prediction confidence, uncertainty, and plausibility<\/span><\/li><li>Flag borderline or low-trust outputs for human review<\/li><\/ul><h6>Feedback &amp; Adaptation<\/h6><ul><li><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400;\">Use actual outcomes \/ ground truth to retrain models<\/span><\/li><li>Monitor drift, recalibrate fusion weights<\/li><li>Adapt pipeline dynamically (e.g. drop low-value modalities, adjust sampling)<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-84d480f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"84d480f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5e0872b\" data-id=\"5e0872b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-db1773f elementor-widget elementor-widget-text-editor\" data-id=\"db1773f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4>Turning Chaos into Clarity: Key Techniques &amp; Best Practices<\/h4><h5>Windowed Aggregation &amp; Temporal Alignment<\/h5><h5><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400; font-size: 16px;\">Group disparate streams into fixed-length or sliding windows (e.g. 30 sec, 5 minutes) so different modalities align. This ensures features computed at a shared time basis.<\/span><\/h5><p>\u00a0<\/p><h5>Confidence-weighted Fusion<\/h5><h5><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400; font-size: 16px;\">Assign reliability scores to each modality (based on signal strength, sensor health, missingness) and let the model dynamically weight them during fusion.<\/span><\/h5><p>\u00a0<\/p><h5>Attention &amp; Cross-Modal Transformers<\/h5><h5><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400; font-size: 16px;\">Modern architectures let the model attend to the most relevant inputs from each modality. Cross-modal attention helps the model learn interaction patterns (e.g. when imagery + sensor spike = event).<\/span><\/h5><p>\u00a0<\/p><h5>Denoising &amp; Robust Encoders<\/h5><h5><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400; font-size: 16px;\">Autoencoders, variational models, or denoising encoders help suppress noise and produce stable embeddings even under missing data.<\/span><\/h5><p>\u00a0<\/p><h5>Uncertainty Estimation<\/h5><h5><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400; font-size: 16px;\">Use Bayesian neural nets, Monte Carlo dropout, or ensemble models to estimate prediction uncertainty, which is especially important when fusing noisy modalities.<\/span><\/h5><p>\u00a0<\/p><h5>Drift Detection &amp; Calibration<\/h5><h5><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400; font-size: 16px;\">Continuously monitor input distributions and model outputs. If drift is detected (e.g. new sensor behavior, environmental shifts), trigger retraining or recalibration.<\/span><\/h5><p>\u00a0<\/p><h5>Human-in-the-loop &amp; Explainability<\/h5><h5><span style=\"color: #7a7a7a; font-family: Roboto, sans-serif; font-weight: 400; font-size: 16px;\">For critical predictions, provide interpretable insights into which modalities or features drove a decision. Allow human override or feedback.<\/span><\/h5>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-eb86901 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"eb86901\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4e1174e\" data-id=\"4e1174e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a64c1dc elementor-widget elementor-widget-text-editor\" data-id=\"a64c1dc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h4>A Concrete Use Case: Fault Prediction in Industrial Equipment<\/h4><p>Imagine an industrial plant with:<\/p><ul><li>Vibration sensors measuring mechanical stress<\/li><li>Thermal sensors monitoring temperature of motors<\/li><li>Drone imagery scanning the plant floor for anomalies<\/li><li>Operational logs recording motor loads<\/li><\/ul><p>Here\u2019s how the pipeline might behave:<\/p><p><b>Chaos stage<\/b>: Vibration data streams every second, thermal sensors every 5 sec, drone images every hour, logs intermittently.<\/p><p><b>Alignment<\/b>: Resample all data to 1-minute windows, aggregate statistics.<\/p><p><b>Feature extraction<\/b>:<\/p><ul><li>Vibration \u2192 RMS, spectral features<\/li><li>Thermal \u2192 temperature trends, spikes<\/li><li>Imagery \u2192 detect hot spots, cracks<\/li><li>Logs \u2192 usage patterns<\/li><\/ul><p><b>Fusion<\/b>: Cross-modal attention combines signals, emphasizing vibration + thermal when imagery data is stale<\/p><p><b>Prediction<\/b>: Pipeline forecasts probability of motor failure within next 24 hours<\/p><p><b>Uncertainty &amp; feedback<\/b>: If uncertainty too high, human inspection is triggered. Over time, actual failures feed back to update model.<\/p><p>This yields predictive clarity \u2014 you can act ahead of failure rather than react after.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-995f313 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"995f313\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0793bc7\" data-id=\"0793bc7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e01d607 elementor-widget elementor-widget-text-editor\" data-id=\"e01d607\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h5>Why This Matters<\/h5><ul><li><b>Proactive decision-making<\/b>: Instead of reacting to disasters, systems anticipate them<\/li><li><b>Resource efficiency<\/b>: Focus attention where risk is highest<\/li><li><b>Robustness to missing data<\/b>: Even when one input fails, system can fall back to others<\/li><li><b>Scalable intelligence<\/b>: Supports many sensors, modalities, and environments<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>In modern systems, raw data rarely arrives in tidy, well-structured form. It\u2019s messy, asynchronous, multimodal, and often noisy. But when properly fused and refined within a Generative AI (Gen AI) Fusion Pipeline, that chaos becomes the backbone of predictive clarity: the ability to forecast events, anticipate anomalies, and guide decisions with confidence. In this blog, [&hellip;]<\/p>\n <a href=\"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/\" class=\"ReadMore\" title=\"Read More\">Read More<\/a>","protected":false},"author":8,"featured_media":2782,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.5.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>From Data Chaos to Predictive Clarity Inside the Gen AI Fusion Pipeline - Niraltek Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"From Data Chaos to Predictive Clarity Inside the Gen AI Fusion Pipeline - Niraltek Blog\" \/>\n<meta property=\"og:description\" content=\"In modern systems, raw data rarely arrives in tidy, well-structured form. It\u2019s messy, asynchronous, multimodal, and often noisy. But when properly fused and refined within a Generative AI (Gen AI) Fusion Pipeline, that chaos becomes the backbone of predictive clarity: the ability to forecast events, anticipate anomalies, and guide decisions with confidence. 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It\u2019s messy, asynchronous, multimodal, and often noisy. But when properly fused and refined within a Generative AI (Gen AI) Fusion Pipeline, that chaos becomes the backbone of predictive clarity: the ability to forecast events, anticipate anomalies, and guide decisions with confidence. In this blog, [&hellip;]","og_url":"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/","og_site_name":"Niraltek Blog","article_published_time":"2025-10-15T10:24:02+00:00","article_modified_time":"2025-10-15T10:31:27+00:00","og_image":[{"width":1280,"height":768,"url":"https:\/\/www.niraltek.com\/blog\/wp-content\/uploads\/2025\/10\/From-Data-Chaos-to-Predictive-Clarity-Inside-the-Gen-AI-Fusion-Pipeline.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_misc":{"Written by":"Arun Karthik","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebSite","@id":"https:\/\/www.niraltek.com\/blog\/#website","url":"https:\/\/www.niraltek.com\/blog\/","name":"Niraltek Blog","description":"Niraltek, IOT, BLOGS","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.niraltek.com\/blog\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"ImageObject","@id":"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/#primaryimage","inLanguage":"en-US","url":"https:\/\/www.niraltek.com\/blog\/wp-content\/uploads\/2025\/10\/From-Data-Chaos-to-Predictive-Clarity-Inside-the-Gen-AI-Fusion-Pipeline.png","contentUrl":"https:\/\/www.niraltek.com\/blog\/wp-content\/uploads\/2025\/10\/From-Data-Chaos-to-Predictive-Clarity-Inside-the-Gen-AI-Fusion-Pipeline.png","width":1280,"height":768},{"@type":"WebPage","@id":"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/#webpage","url":"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/","name":"From Data Chaos to Predictive Clarity Inside the Gen AI Fusion Pipeline - Niraltek Blog","isPartOf":{"@id":"https:\/\/www.niraltek.com\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/#primaryimage"},"datePublished":"2025-10-15T10:24:02+00:00","dateModified":"2025-10-15T10:31:27+00:00","author":{"@id":"https:\/\/www.niraltek.com\/blog\/#\/schema\/person\/1602d07bc489f15f6e9651bd767e93d0"},"breadcrumb":{"@id":"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.niraltek.com\/blog\/from-data-chaos-to-predictive-clarity-inside-the-gen-ai-fusion-pipeline\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.niraltek.com\/blog\/"},{"@type":"ListItem","position":2,"name":"From Data Chaos to Predictive Clarity Inside the Gen AI Fusion Pipeline"}]},{"@type":"Person","@id":"https:\/\/www.niraltek.com\/blog\/#\/schema\/person\/1602d07bc489f15f6e9651bd767e93d0","name":"Arun Karthik","image":{"@type":"ImageObject","@id":"https:\/\/www.niraltek.com\/blog\/#personlogo","inLanguage":"en-US","url":"https:\/\/secure.gravatar.com\/avatar\/4f1c1deed4c736b307a4b29176c6f415?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/4f1c1deed4c736b307a4b29176c6f415?s=96&d=mm&r=g","caption":"Arun Karthik"},"sameAs":["https:\/\/www.linkedin.com\/in\/arun-karthik-630273229\/"],"url":"https:\/\/www.niraltek.com\/blog\/author\/arunkarthik\/"}]}},"_links":{"self":[{"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/posts\/2781"}],"collection":[{"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/comments?post=2781"}],"version-history":[{"count":5,"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/posts\/2781\/revisions"}],"predecessor-version":[{"id":2787,"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/posts\/2781\/revisions\/2787"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/media\/2782"}],"wp:attachment":[{"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/media?parent=2781"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/categories?post=2781"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.niraltek.com\/blog\/wp-json\/wp\/v2\/tags?post=2781"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}