{"id":4798,"date":"2026-05-25T22:42:13","date_gmt":"2026-05-25T17:12:13","guid":{"rendered":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/"},"modified":"2026-05-25T22:42:13","modified_gmt":"2026-05-25T17:12:13","slug":"what-is-machine-learning-guide","status":"publish","type":"post","link":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/","title":{"rendered":"what is machine learning &#8211; Guide"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a1508a064a84\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a1508a064a84\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#Stop_Calling_It_Magic_A_Grumpy_SREs_Guide_to_What_Is_Machine_Learning\" >Stop Calling It Magic: A Grumpy SRE\u2019s Guide to What Is Machine Learning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#The_Documentation_Is_Lying_To_You\" >The Documentation Is Lying To You<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#The_Meat_How_It_Actually_Works_Without_the_Fluff\" >The Meat: How It Actually Works (Without the Fluff)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#The_Infrastructure_Tax_Why_Your_Kubelet_Is_Crying\" >The Infrastructure Tax: Why Your Kubelet Is Crying<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#Feature_Engineering_The_Real_Work\" >Feature Engineering: The Real Work<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#The_%E2%80%9CGotcha%E2%80%9D_Training-Serving_Skew\" >The &#8220;Gotcha&#8221;: Training-Serving Skew<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#The_Lifecycle_of_a_Model_The_SRE_Version\" >The Lifecycle of a Model (The SRE Version)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#What_Is_Machine_Learning_Its_a_Maintenance_Burden\" >What Is Machine Learning? It&#8217;s a Maintenance Burden.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#The_Technical_Debt_of_%E2%80%9CIntelligence%E2%80%9D\" >The Technical Debt of &#8220;Intelligence&#8221;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#The_Wrap-up\" >The Wrap-up<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#Related_Articles\" >Related Articles<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Stop_Calling_It_Magic_A_Grumpy_SREs_Guide_to_What_Is_Machine_Learning\"><\/span>Stop Calling It Magic: A Grumpy SRE\u2019s Guide to What Is Machine Learning<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>It was 3 AM on a Tuesday in 2019. We had just deployed a &#8220;smart&#8221; auto-scaler for our edge nodes. The idea was simple: use a lightweight ML model to predict traffic spikes and spin up instances <i>before<\/i> the load hit. Instead, the model saw a routine cron job, interpreted the slight CPU blip as an exponential curve, and tried to provision 4,000 AWS <code>c5.4xlarge<\/code> instances in the <code>us-east-1<\/code> region. We didn&#8217;t have the quota, but the attempt alone triggered a cascade of API rate limits that locked our entire control plane. <\/p>\n<p>AWS throttled us. Our monitoring went dark. Because the &#8220;smart&#8221; scaler was also responsible for health checks, it started marking healthy nodes as &#8220;stale&#8221; because it couldn&#8217;t talk to the AWS API. We spent six hours manually killing rogue processes while the CFO watched the billing dashboard climb like a SpaceX rocket. That was my introduction to &#8220;AI-driven operations.&#8221; It wasn&#8217;t intelligent; it was a feedback loop with a credit card attached. If you want to know <b>what is<\/b> machine learning, start there: it is a system that fails in ways your unit tests can\u2019t catch.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Documentation_Is_Lying_To_You\"><\/span>The Documentation Is Lying To You<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you search for &#8220;what is machine learning,&#8221; you\u2019ll find a thousand blogs talking about &#8220;mimicking human intelligence&#8221; or &#8220;neural pathways.&#8221; That\u2019s marketing garbage. It\u2019s designed to sell VC seats and SaaS subscriptions. In reality, machine learning is just high-dimensional curve fitting. It\u2019s a way to generate a function $f(x) = y$ when the logic is too messy for a human to write in a <code>switch<\/code> statement. <\/p>\n<p>Most documentation ignores the infrastructure tax. They show you a Jupyter notebook where everything works on a <code>.csv<\/code> file stored on a laptop. They don&#8217;t tell you about the 15GB Docker images, the <code>glibc<\/code> version mismatches in your base image, or the fact that <code>nvidia-smi<\/code> will be the most important command in your troubleshooting toolkit. We\u2019re moving away from &#8220;if-this-then-that&#8221; and moving toward &#8220;if-this-is-statistically-likely-then-maybe-that.&#8221; It\u2019s a nightmare for anyone who cares about determinism.<\/p>\n<blockquote><p>\n    <strong>Note to self:<\/strong> Never trust a model that hasn&#8217;t been tested against a dataset containing null bytes or 4-byte UTF-8 characters. It will segfault your inference engine.\n<\/p><\/blockquote>\n<h2><span class=\"ez-toc-section\" id=\"The_Meat_How_It_Actually_Works_Without_the_Fluff\"><\/span>The Meat: How It Actually Works (Without the Fluff)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>At its core, machine learning is a three-part problem: Data, Model, and Compute. If you\u2019re an SRE, you mostly care about the Compute and the Data pipeline, because that\u2019s what breaks at 2:00 PM on a Friday. <\/p>\n<p>To understand <b>what is<\/b> machine learning, you have to look at the training phase. You take a massive pile of data\u2014let\u2019s say 500GB of JSON logs from <code>api.stripe.com<\/code>\u2014and you feed it into an algorithm. The algorithm tries to find patterns. It assigns &#8220;weights&#8221; (numbers) to different inputs. If it\u2019s trying to predict fraud, it might give a high weight to &#8220;IP address from a known data center&#8221; and a low weight to &#8220;User has been active for 5 years.&#8221;<\/p>\n<pre><code>\n# This is what people think ML is\nimport torch\nimport torch.nn as nn\n\nclass SimpleModel(nn.Module):\n    def __init__(self):\n        super(SimpleModel, self).__init__()\n        self.layer1 = nn.Linear(128, 64)\n        self.layer2 = nn.Linear(64, 1)\n\n    def forward(self, x):\n        return torch.sigmoid(self.layer2(torch.relu(self.layer1(x))))\n\n# This is what ML actually is in production\ntry:\n    model.load_state_dict(torch.load('\/mnt\/models\/v2\/weights.pth'))\nexcept RuntimeError as e:\n    print(f\"Incompatible weights. Did someone change the hidden layer size without telling DevOps? {e}\")\n    sys.exit(1)\n<\/code><\/pre>\n<p>The &#8220;Learning&#8221; part is just an optimization loop. The model makes a guess, calculates how wrong it was (the &#8220;Loss&#8221;), and uses backpropagation to tweak the weights. It does this millions of times. Eventually, you get a binary file\u2014a &#8220;model&#8221;\u2014that you can push to production. This file is a black box. You can&#8217;t <code>grep<\/code> it. You can&#8217;t <code>diff<\/code> it in a meaningful way. You just have to trust the validation metrics.<\/p>\n<ul>\n<li><b>Supervised Learning:<\/b> You give the model the answers. &#8220;Here is a picture of a cat, it is a cat.&#8221; This is expensive because humans have to label the data.<\/li>\n<li><b>Unsupervised Learning:<\/b> You give the model data and say &#8220;find something interesting.&#8221; This usually results in the model finding that &#8220;users who buy shoes also buy socks,&#8221; which your marketing team already knew.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"The_Infrastructure_Tax_Why_Your_Kubelet_Is_Crying\"><\/span>The Infrastructure Tax: Why Your Kubelet Is Crying<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>When you ask &#8220;what is machine learning&#8221; from an operational perspective, the answer is &#8220;a resource hog.&#8221; Traditional microservices are easy to scale. You look at CPU and memory. ML adds a third dimension: the GPU. <\/p>\n<p>If you\u2019re running inference on a CPU, your p99 latency will likely be garbage (300ms+). If you move to GPUs, you\u2019re now dealing with <code>nvidia-container-runtime<\/code>, specific CUDA versions (e.g., 12.1 vs 11.8), and the fact that a single <code>A100<\/code> instance costs more per hour than your entire staging environment. <\/p>\n<p>I once saw a team try to deploy a BERT-based NLP model into a standard Kubernetes cluster without setting resource limits. The model tried to pre-allocate 12GB of VRAM. The node only had 8GB. The <code>Kubelet<\/code> didn&#8217;t just kill the pod; the entire NVIDIA driver hung, requiring a hard reboot of the bare-metal host. We lost three other production pods on that node. <\/p>\n<pre><code>\n# A snippet of the YAML-hell you'll encounter\nresources:\n  limits:\n    nvidia.com\/gpu: 1\n  requests:\n    memory: \"16Gi\"\n    cpu: \"4\"\n# Pro-tip: Always set shm-size for PyTorch. \n# Default Docker shm-size is 64MB, which will OOM-kill your dataloaders.\n<\/code><\/pre>\n<h2><span class=\"ez-toc-section\" id=\"Feature_Engineering_The_Real_Work\"><\/span>Feature Engineering: The Real Work<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Data scientists spend 90% of their time on &#8220;Feature Engineering.&#8221; This is a fancy term for &#8220;cleaning up the mess in the database.&#8221; If your input data is <code>null<\/code>, or if a dev changed a column name in <code>localhost:5432<\/code> without updating the ETL pipeline, the model will fail. But it won&#8217;t fail with a <code>500 Internal Server Error<\/code>. It will fail silently by giving a wrong answer with 99% confidence.<\/p>\n<p>Consider a recommendation engine. You need to feed it &#8220;user_age,&#8221; &#8220;last_purchase_timestamp,&#8221; and &#8220;browser_type.&#8221; <\/p>\n<ul>\n<li>What if &#8220;user_age&#8221; is missing? Do you use 0? The average? -1? Each choice changes the model&#8217;s output.<\/li>\n<li>What if the timestamp is in UTC in the database but the model was trained on PST?<\/li>\n<li>What if the &#8220;browser_type&#8221; is &#8220;Mozilla\/5.0&#8230;&#8221; and the model only expects &#8220;Chrome&#8221; or &#8220;Safari&#8221;?<\/li>\n<li>What if the data is skewed because 80% of your traffic comes from bots?<\/li>\n<li>What if the upstream API at <code>api.segment.io<\/code> changes its payload format?<\/li>\n<\/ul>\n<p>This is why ML is hard. It\u2019s not the math. It\u2019s the data contract. In a standard app, a broken contract triggers an exception. In ML, a broken contract triggers a bad business decision.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_%E2%80%9CGotcha%E2%80%9D_Training-Serving_Skew\"><\/span>The &#8220;Gotcha&#8221;: Training-Serving Skew<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This is the silent killer of ML projects. Training-serving skew happens when the data the model sees during training is different from the data it sees in production. <\/p>\n<p>Imagine you train a model to predict server failures. You use historical data from your <code>Prometheus<\/code> archives. In the archives, the data is aggregated every 5 minutes. But in production, your real-time monitoring feeds the model data every 10 seconds. The model, expecting 5-minute averages, sees the 10-second spikes and panics. It starts flagging every server as &#8220;about to explode.&#8221; <\/p>\n<p>You can&#8217;t fix this with a better algorithm. You fix it by ensuring your feature pipeline is identical in both environments. This usually means using a &#8220;Feature Store,&#8221; which is just a very expensive database that both your training scripts and your production API can query. <\/p>\n<blockquote><p>\n    <strong>Pro-tip:<\/strong> If someone suggests building a custom feature store in-house, quit your job. Use an off-the-shelf solution or just use a well-indexed Postgres table. Don&#8217;t reinvent the wheel with more YAML.\n<\/p><\/blockquote>\n<h2><span class=\"ez-toc-section\" id=\"The_Lifecycle_of_a_Model_The_SRE_Version\"><\/span>The Lifecycle of a Model (The SRE Version)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Most people think the lifecycle is: <i>Research -> Train -> Deploy<\/i>.<br \/>\nThe reality is more like: <i>Data Leakage -> OOM Kill -> Dependency Hell -> Silent Failure -> Rollback<\/i>.<\/p>\n<ol>\n<li><b>Data Collection:<\/b> You realize your <code>S3<\/code> buckets are a mess and half the logs are missing.<\/li>\n<li><b>Training:<\/b> You burn $5,000 in AWS credits to get a model that is 2% better than a random guess.<\/li>\n<li><b>Packaging:<\/b> You try to wrap the model in a Flask API. You realize <code>pandas<\/code> and <code>numpy<\/code> add 800MB to your image size.<\/li>\n<li><b>Deployment:<\/b> You push to production. The <code>livenessProbe<\/code> fails because the model takes 45 seconds to load into memory.<\/li>\n<li><b>Monitoring:<\/b> You realize you have no idea if the model is working. You start logging every prediction to <code>BigQuery<\/code> for &#8220;later analysis.&#8221;<\/li>\n<li><b>Drift:<\/b> Three weeks later, the model&#8217;s accuracy drops because the world changed (e.g., a holiday season started) and the model doesn&#8217;t know what a &#8220;Black Friday&#8221; is.<\/li>\n<\/ol>\n<h2><span class=\"ez-toc-section\" id=\"What_Is_Machine_Learning_Its_a_Maintenance_Burden\"><\/span>What Is Machine Learning? It&#8217;s a Maintenance Burden.<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you can solve a problem with a <code>regex<\/code>, do it. If you can solve it with a <code>JOIN<\/code>, do it. If you can solve it with a set of hard-coded rules, do it. Machine learning should be your absolute last resort. Why? Because you can&#8217;t debug it. <\/p>\n<p>When a customer asks &#8220;Why was my account flagged for fraud?&#8221;, and your answer is &#8220;The weights in the third hidden layer of our neural network were slightly higher for your specific latency profile,&#8221; you haven&#8217;t solved a problem. You&#8217;ve just automated an excuse. <\/p>\n<p>We use ML at my current gig for image compression. It\u2019s great. If it fails, the image just looks a bit pixelated. The stakes are low. But when people talk about using ML for &#8220;automated incident response&#8221; or &#8220;AI-driven security,&#8221; I reach for my pager. Those systems are brittle. They don&#8217;t handle &#8220;unknown unknowns.&#8221; They only know what they&#8217;ve seen before. And in the world of SRE, the thing that breaks your system is almost always something you&#8217;ve never seen before.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Technical_Debt_of_%E2%80%9CIntelligence%E2%80%9D\"><\/span>The Technical Debt of &#8220;Intelligence&#8221;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Google published a paper years ago called &#8220;Machine Learning: The High Interest Credit Card of Technical Debt.&#8221; Every SRE should read it. It points out that the actual ML code is a tiny fraction of the system. The rest is configuration, data collection, feature extraction, monitoring, and infrastructure management. <\/p>\n<p>When you add a model to your stack, you aren&#8217;t just adding a library. You&#8217;re adding a dependency on the specific distribution of your input data. If your marketing team runs a campaign in a new country, your model might break. If your frontend team changes the UI and users start clicking differently, your model might break. It is the only type of code that &#8220;rots&#8221; even if you don&#8217;t change a single line of it.<\/p>\n<pre><code>\n# Monitoring for drift isn't just checking CPU. \n# It's checking the distribution of your outputs.\ndef check_model_drift(current_predictions, baseline_distribution):\n    # Use something like Kolmogorov-Smirnov test\n    drift_score = ks_test(current_predictions, baseline_distribution)\n    if drift_score > 0.05:\n        trigger_alert(\"Model is hallucinating or the world changed.\")\n<\/code><\/pre>\n<h2><span class=\"ez-toc-section\" id=\"The_Wrap-up\"><\/span>The Wrap-up<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Machine learning is just a way to trade code complexity for data complexity. You stop writing logic and start managing pipelines. It\u2019s not a silver bullet; it\u2019s a heavy, expensive, non-deterministic cannon that requires a team of engineers to keep it pointed in the right direction. If you\u2019re going to use it, make sure the problem you\u2019re solving is worth the 3 AM pages and the GPU bill. Most of the time, it isn&#8217;t. <\/p>\n<p>Don&#8217;t build a &#8220;smart&#8221; system until you have a &#8220;reliable&#8221; one. A simple script that works 100% of the time is infinitely better than a &#8220;neural network&#8221; that works 95% of the time and fails spectacularly the other 5%.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Related_Articles\"><\/span>Related Articles<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Explore more insights and best practices:<\/p>\n<ul>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/10-react-best-practices-for-high-performance-apps\/\">10 React Best Practices For High Performance Apps<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/kubernetes-github-guide\/\">Kubernetes Github Guide<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/what-is-react-a-beginners-guide-to-the-js-library\/\">What Is React A Beginners Guide To The Js Library<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Stop Calling It Magic: A Grumpy SRE\u2019s Guide to What Is Machine Learning It was 3 AM on a Tuesday in 2019. We had just deployed a &#8220;smart&#8221; auto-scaler for our edge nodes. The idea was simple: use a lightweight ML model to predict traffic spikes and spin up instances before the load hit. Instead, &#8230; <a title=\"what is machine learning &#8211; Guide\" class=\"read-more\" href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/\" aria-label=\"Read more  on what is machine learning &#8211; Guide\">Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4798","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>what is machine learning - Guide - ITSupportWale<\/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:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"what is machine learning - Guide - ITSupportWale\" \/>\n<meta property=\"og:description\" content=\"Stop Calling It Magic: A Grumpy SRE\u2019s Guide to What Is Machine Learning It was 3 AM on a Tuesday in 2019. We had just deployed a &#8220;smart&#8221; auto-scaler for our edge nodes. The idea was simple: use a lightweight ML model to predict traffic spikes and spin up instances before the load hit. Instead, ... Read more\" \/>\n<meta property=\"og:url\" content=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/\" \/>\n<meta property=\"og:site_name\" content=\"ITSupportWale\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/Itsupportwale-298547177495978\" \/>\n<meta property=\"article:published_time\" content=\"2026-05-25T17:12:13+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/itsupportwale.com\/blog\/wp-content\/uploads\/2021\/05\/android-chrome-512x512-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"512\" \/>\n\t<meta property=\"og:image:height\" content=\"512\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Techie\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Techie\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"10 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/\"},\"author\":{\"name\":\"Techie\",\"@id\":\"https:\/\/itsupportwale.com\/blog\/#\/schema\/person\/8c5a2b3d36396e0a8fd91ec8242fd46d\"},\"headline\":\"what is machine learning &#8211; Guide\",\"datePublished\":\"2026-05-25T17:12:13+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/\"},\"wordCount\":1786,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/itsupportwale.com\/blog\/#organization\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/\",\"url\":\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/\",\"name\":\"what is machine learning - Guide - ITSupportWale\",\"isPartOf\":{\"@id\":\"https:\/\/itsupportwale.com\/blog\/#website\"},\"datePublished\":\"2026-05-25T17:12:13+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/itsupportwale.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"what is machine learning &#8211; Guide\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/itsupportwale.com\/blog\/#website\",\"url\":\"https:\/\/itsupportwale.com\/blog\/\",\"name\":\"ITSupportWale\",\"description\":\"Tips, Tricks, Fixed-Errors, Tutorials &amp; Guides\",\"publisher\":{\"@id\":\"https:\/\/itsupportwale.com\/blog\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/itsupportwale.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/itsupportwale.com\/blog\/#organization\",\"name\":\"itsupportwale\",\"url\":\"https:\/\/itsupportwale.com\/blog\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/itsupportwale.com\/blog\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/itsupportwale.com\/blog\/wp-content\/uploads\/2023\/09\/cropped-Logo-trans-without-slogan.png\",\"contentUrl\":\"https:\/\/itsupportwale.com\/blog\/wp-content\/uploads\/2023\/09\/cropped-Logo-trans-without-slogan.png\",\"width\":1119,\"height\":144,\"caption\":\"itsupportwale\"},\"image\":{\"@id\":\"https:\/\/itsupportwale.com\/blog\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/Itsupportwale-298547177495978\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/itsupportwale.com\/blog\/#\/schema\/person\/8c5a2b3d36396e0a8fd91ec8242fd46d\",\"name\":\"Techie\",\"sameAs\":[\"https:\/\/itsupportwale.com\",\"iswblogadmin\"],\"url\":\"https:\/\/itsupportwale.com\/blog\/author\/iswblogadmin\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"what is machine learning - Guide - ITSupportWale","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/","og_locale":"en_US","og_type":"article","og_title":"what is machine learning - Guide - ITSupportWale","og_description":"Stop Calling It Magic: A Grumpy SRE\u2019s Guide to What Is Machine Learning It was 3 AM on a Tuesday in 2019. We had just deployed a &#8220;smart&#8221; auto-scaler for our edge nodes. The idea was simple: use a lightweight ML model to predict traffic spikes and spin up instances before the load hit. Instead, ... Read more","og_url":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/","og_site_name":"ITSupportWale","article_publisher":"https:\/\/www.facebook.com\/Itsupportwale-298547177495978","article_published_time":"2026-05-25T17:12:13+00:00","og_image":[{"width":512,"height":512,"url":"https:\/\/itsupportwale.com\/blog\/wp-content\/uploads\/2021\/05\/android-chrome-512x512-1.png","type":"image\/png"}],"author":"Techie","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Techie","Est. reading time":"10 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#article","isPartOf":{"@id":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/"},"author":{"name":"Techie","@id":"https:\/\/itsupportwale.com\/blog\/#\/schema\/person\/8c5a2b3d36396e0a8fd91ec8242fd46d"},"headline":"what is machine learning &#8211; Guide","datePublished":"2026-05-25T17:12:13+00:00","mainEntityOfPage":{"@id":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/"},"wordCount":1786,"commentCount":0,"publisher":{"@id":"https:\/\/itsupportwale.com\/blog\/#organization"},"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/","url":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/","name":"what is machine learning - Guide - ITSupportWale","isPartOf":{"@id":"https:\/\/itsupportwale.com\/blog\/#website"},"datePublished":"2026-05-25T17:12:13+00:00","breadcrumb":{"@id":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/itsupportwale.com\/blog\/"},{"@type":"ListItem","position":2,"name":"what is machine learning &#8211; Guide"}]},{"@type":"WebSite","@id":"https:\/\/itsupportwale.com\/blog\/#website","url":"https:\/\/itsupportwale.com\/blog\/","name":"ITSupportWale","description":"Tips, Tricks, Fixed-Errors, Tutorials &amp; Guides","publisher":{"@id":"https:\/\/itsupportwale.com\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/itsupportwale.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/itsupportwale.com\/blog\/#organization","name":"itsupportwale","url":"https:\/\/itsupportwale.com\/blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/itsupportwale.com\/blog\/#\/schema\/logo\/image\/","url":"https:\/\/itsupportwale.com\/blog\/wp-content\/uploads\/2023\/09\/cropped-Logo-trans-without-slogan.png","contentUrl":"https:\/\/itsupportwale.com\/blog\/wp-content\/uploads\/2023\/09\/cropped-Logo-trans-without-slogan.png","width":1119,"height":144,"caption":"itsupportwale"},"image":{"@id":"https:\/\/itsupportwale.com\/blog\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/Itsupportwale-298547177495978"]},{"@type":"Person","@id":"https:\/\/itsupportwale.com\/blog\/#\/schema\/person\/8c5a2b3d36396e0a8fd91ec8242fd46d","name":"Techie","sameAs":["https:\/\/itsupportwale.com","iswblogadmin"],"url":"https:\/\/itsupportwale.com\/blog\/author\/iswblogadmin\/"}]}},"_links":{"self":[{"href":"https:\/\/itsupportwale.com\/blog\/wp-json\/wp\/v2\/posts\/4798","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/itsupportwale.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/itsupportwale.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/itsupportwale.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/itsupportwale.com\/blog\/wp-json\/wp\/v2\/comments?post=4798"}],"version-history":[{"count":0,"href":"https:\/\/itsupportwale.com\/blog\/wp-json\/wp\/v2\/posts\/4798\/revisions"}],"wp:attachment":[{"href":"https:\/\/itsupportwale.com\/blog\/wp-json\/wp\/v2\/media?parent=4798"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/itsupportwale.com\/blog\/wp-json\/wp\/v2\/categories?post=4798"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/itsupportwale.com\/blog\/wp-json\/wp\/v2\/tags?post=4798"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}