{"id":29289,"date":"2025-06-11T14:08:40","date_gmt":"2025-06-11T08:38:40","guid":{"rendered":"https:\/\/opstree.com\/blog\/?p=29289"},"modified":"2025-06-11T14:08:55","modified_gmt":"2025-06-11T08:38:55","slug":"edge-ai-running-tensorflow-models-on-iot-devices","status":"publish","type":"post","link":"https:\/\/opstree.com\/blog\/2025\/06\/11\/edge-ai-running-tensorflow-models-on-iot-devices\/","title":{"rendered":"Edge AI: Running TensorFlow Models on IoT Devices"},"content":{"rendered":"<p><span data-contrast=\"none\">Your smart thermostat senses a temperature drop before you notice. Your camera recognizes a familiar face the second it appears. And none of it goes through the cloud. That\u2019s the power of Edge AI with TensorFlow, where machine learning runs locally on IoT devices, making them faster, more private, and incredibly efficient.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">In this blog, we\u2019ll explore how TensorFlow Lite models are optimized for edge hardware, the challenges of deploying lightweight AI models on embedded systems, and the benefits of real-time AI inference at the edge for smart devices.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><!--more--><\/p>\n<p><b><i><span data-contrast=\"none\">Why Edge AI?<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:400,&quot;335559739&quot;:120}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Traditional AI models depend on cloud computing, which involves sending data to remote servers for processing. Although effective, this method comes with certain limitations:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><i><span data-contrast=\"none\">Latency:<\/span><\/i><\/b><span data-contrast=\"none\"> Critical applications (e.g., autonomous drones, industrial robots) can\u2019t afford delays.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:220,&quot;335559739&quot;:220}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><i><span data-contrast=\"none\">Bandwidth Costs:<\/span><\/i><\/b><span data-contrast=\"none\"> Transmitting large volumes of sensor data is expensive.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:220,&quot;335559739&quot;:220}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><i><span data-contrast=\"none\">Privacy &amp; Compliance:<\/span><\/i><\/b><span data-contrast=\"none\"> Sensitive data (e.g., medical diagnostics) shouldn\u2019t leave the device.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:220,&quot;335559739&quot;:220}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"none\">Edge AI addresses these challenges by executing AI models directly on IoT devices, with TensorFlow Google\u2019s open-source machine learning framework playing a crucial role in enabling this shift.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><i><span data-contrast=\"none\">TensorFlow for Edge AI<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">TensorFlow offers two key solutions for edge deployment:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ol>\n<li><b><i><span data-contrast=\"none\"> TensorFlow Lite (TFLite) \u2013 <\/span><\/i><\/b><span data-contrast=\"none\">A lightweight framework optimized for mobile and embedded devices.<\/span><\/li>\n<\/ol>\n<ol start=\"2\">\n<li><b><i><span data-contrast=\"none\"> TensorFlow Micro (TF Micro) \u2013<\/span><\/i><\/b><span data-contrast=\"none\"> A stripped-down version for microcontrollers with limited resources.<\/span><\/li>\n<\/ol>\n<p><b><i><span data-contrast=\"none\">Running TensorFlow Lite Models on Edge Hardware<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Deploying TFLite models involves:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ol>\n<li><b><i><span data-contrast=\"none\"> Model Optimization:<\/span><\/i><\/b> <span data-contrast=\"none\">Techniques like quantization (reducing precision from 32-bit to 8-bit) shrink model size without significant accuracy loss.<\/span><\/li>\n<\/ol>\n<ol start=\"2\">\n<li><b><i><span data-contrast=\"none\"> Hardware Acceleration:<\/span><\/i><\/b><span data-contrast=\"none\"> Leveraging edge processors like Google\u2019s Coral Edge TPU or NVIDIA Jetson for faster inference.<\/span><\/li>\n<\/ol>\n<ol start=\"3\">\n<li><b><i><span data-contrast=\"none\"> Cross-Platform Compatibility:<\/span><\/i><\/b><span data-contrast=\"none\"> TFLite runs on Linux, Android, iOS, and even Raspberry Pi.<\/span><\/li>\n<\/ol>\n<p><b><i><span data-contrast=\"none\">Example Use Case: <\/span><\/i><\/b><span data-contrast=\"none\">A smart camera using TFLite for real-time object detection without cloud connectivity.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><i><span data-contrast=\"none\">Deploying Lightweight AI Models on Embedded Systems<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Embedded devices (e.g., ARM Cortex-M microcontrollers) have strict memory and power constraints. Here\u2019s how TensorFlow adapts:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"7\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><i><span data-contrast=\"none\">Pruning: <\/span><\/i><\/b><span data-contrast=\"none\">Removing redundant neurons to reduce model size.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:220,&quot;335559739&quot;:220}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"7\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><i><span data-contrast=\"none\">Knowledge Distillation:<\/span><\/i><\/b><span data-contrast=\"none\"> Training a smaller model to mimic a larger one.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:220,&quot;335559739&quot;:220}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"7\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><b><i><span data-contrast=\"none\">TF Micro:<\/span><\/i><\/b><span data-contrast=\"none\"> Supports models as small as 20KB, ideal for wearables and sensors.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:220,&quot;335559739&quot;:220}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><b><i><span data-contrast=\"none\">Example Use Case: <\/span><\/i><\/b><span data-contrast=\"none\">Predictive maintenance sensors in factories analyzing vibration data locally.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><i><span data-contrast=\"none\">Challenges of Edge AI Deployment<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Despite its advantages, on-device machine learning presents hurdles:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"9\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><i><span data-contrast=\"none\">Limited Compute Resources: <\/span><\/i><\/b><span data-contrast=\"none\">Not all models can run efficiently on low-power chips.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:220,&quot;335559739&quot;:220}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"10\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><i><span data-contrast=\"none\">Model Compression Trade-offs: <\/span><\/i><\/b><span data-contrast=\"none\">Aggressive quantization may hurt accuracy.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:220,&quot;335559739&quot;:220}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"11\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><b><i><span data-contrast=\"none\">Fragmented Hardware Ecosystem: <\/span><\/i><\/b><span data-contrast=\"none\">Optimizing for different edge devices (GPUs, TPUs, MCUs) requires customization.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:220,&quot;335559739&quot;:220}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><b><i><span data-contrast=\"none\">Solution:<\/span><\/i><\/b><span data-contrast=\"none\"> TensorFlow\u2019s Model Optimization Toolkit automates pruning and quantization, balancing performance and efficiency.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><i><span data-contrast=\"none\">Real-Time AI Inference at the Edge for Smart Devices<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Industries benefit from real-time AI inference at the edge:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ol>\n<li><b><i><span data-contrast=\"none\"> Healthcare:<\/span><\/i><\/b><span data-contrast=\"none\"> Portable ultrasound devices diagnosing conditions instantly.<\/span><\/li>\n<li><b><i><span data-contrast=\"none\"> Retail:<\/span><\/i><\/b><span data-contrast=\"none\"> Smart shelves detecting out-of-stock items autonomously.<\/span><\/li>\n<li><b><i><span data-contrast=\"none\"> Agriculture: <\/span><\/i><\/b><span data-contrast=\"none\">Drones identifying crop diseases in the field.<\/span><\/li>\n<\/ol>\n<p><b><i><span data-contrast=\"none\">Conclusion<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Edge AI with TensorFlow is transforming IoT by enabling AI on IoT devices without cloud dependency. Whether it\u2019s running TensorFlow Lite models on edge hardware or deploying lightweight AI models on embedded systems, businesses gain speed, security, and scalability.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">For decision-makers, the key takeaway is clear: Investing in on-device machine learning today will drive the smart, autonomous systems of tomorrow.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><i><span data-contrast=\"none\">Frequently Asked Questions<\/span><\/i><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ol>\n<li><b><i><span data-contrast=\"none\"> What is Edge AI, and how does TensorFlow support it?<\/span><\/i><\/b><\/li>\n<\/ol>\n<p><b><i><span data-contrast=\"none\">A: <\/span><\/i><\/b><span data-contrast=\"none\">Edge AI runs machine learning models directly on IoT devices instead of the cloud. TensorFlow supports this via TensorFlow Lite (for mobile\/edge devices) and TensorFlow Micro (for microcontrollers), enabling efficient on-device inference.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ol start=\"2\">\n<li><b><i><span data-contrast=\"none\"> Why should businesses deploy AI models on IoT devices instead of the cloud?<\/span><\/i><\/b><\/li>\n<\/ol>\n<p><b><i><span data-contrast=\"none\">A:<\/span><\/i><\/b><span data-contrast=\"none\"> Edge AI reduces latency, enhances data privacy, cuts bandwidth costs, and works offline, critical for real-time applications like industrial automation and healthcare diagnostics.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ol start=\"3\">\n<li><b><i><span data-contrast=\"none\"> How can TensorFlow models be optimized for edge devices?<\/span><\/i><\/b><\/li>\n<\/ol>\n<p><b><i><span data-contrast=\"none\">A: <\/span><\/i><\/b><span data-contrast=\"none\">Techniques like quantization (reducing model precision), pruning (removing unnecessary neurons), and hardware acceleration (using TPUs\/GPUs) help shrink models for edge deployment.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ol start=\"4\">\n<li><b><i><span data-contrast=\"none\"> What are the limitations of running AI on IoT devices?<\/span><\/i><\/b><\/li>\n<\/ol>\n<p><b><i><span data-contrast=\"none\">A: <\/span><\/i><\/b><span data-contrast=\"none\">Limited compute power, memory constraints, and trade-offs between model size and accuracy can be challenges. However, tools like TensorFlow Lite and TensorFlow Model Optimization Toolkit help mitigate these issues.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ol start=\"5\">\n<li><b><i><span data-contrast=\"none\"> Which industries benefit most from Edge AI with TensorFlow?<\/span><\/i><\/b><\/li>\n<\/ol>\n<p><b><i><span data-contrast=\"none\">A: <\/span><\/i><\/b><span data-contrast=\"none\">Manufacturing (predictive maintenance), healthcare (portable diagnostics), retail (smart inventory), and agriculture (drones for crop monitoring) gain significant advantages from real-time, on-device AI.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Your smart thermostat senses a temperature drop before you notice. Your camera recognizes a familiar face the second it appears. And none of it goes through the cloud. That\u2019s the power of Edge AI with TensorFlow, where machine learning runs locally on IoT devices, making them faster, more private, and incredibly efficient.\u00a0 In this blog, &hellip; <a href=\"https:\/\/opstree.com\/blog\/2025\/06\/11\/edge-ai-running-tensorflow-models-on-iot-devices\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Edge AI: Running TensorFlow Models on IoT Devices&#8221;<\/span><\/a><\/p>\n","protected":false},"author":244582688,"featured_media":29290,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false},"version":2}},"categories":[28070474],"tags":[768739547,768739550,768739546,768739548,768739551,768739549],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/opstree.com\/blog\/wp-content\/uploads\/2025\/06\/3.jpg","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/pfDBOm-7Cp","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts\/29289"}],"collection":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/users\/244582688"}],"replies":[{"embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/comments?post=29289"}],"version-history":[{"count":2,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts\/29289\/revisions"}],"predecessor-version":[{"id":29292,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/posts\/29289\/revisions\/29292"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/media\/29290"}],"wp:attachment":[{"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/media?parent=29289"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/categories?post=29289"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/opstree.com\/blog\/wp-json\/wp\/v2\/tags?post=29289"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}